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The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed. In this method, the motion of the airborne maritime surveillance radar platform is firstly compensated for target echoes. Secondly, range envelope alignment is performed to correct the unpredictable range migration of the target after pulse compression. The higher-order phase difference between the adjacent pulses is estimated and compensated. Ultimately, such pulse series are accumulated through azimuth Fourier transform. Traditional methods compensate only for platform motion, limiting their ability to handle variable-speed targets. The proposed algorithm addresses this limitation by compensating for both platform and target motion, significantly improving signal focusing and tracking accuracy. A detailed analysis shows that our algorithm can significantly increase the signal accumulating gain and improve the focusing effect. The simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Third Long March 5B R/B re-entry campaign. • Radar technologies for space surveillance and tracking related applications. • Orbit determination of uncatalogued objects. • Stochastic satellites re-entry monitoring and prediction. • Stochastic ballistic coefficient estimation. The current overpopulation of orbiting objects requires Space Surveillance and Tracking (SST) services. Among these, the monitoring of re-entry events plays a crucial role in ensuring the safety of people and infrastructure on Earth. The work discusses the third Long-March 5B re-entry campaign, which took place from October 31st to November 4th, 2022. This was coordinated, at a European level, by the Space Situational Awareness Centre (C-SSA) of the Italian Air Force (Aeronautica Militare, AM), in collaboration with the AM Flight Test Department, the Italian Space Agency (ASI) and the Astrophysics National Institute (INAF). Support was granted by other Italian institutions, like Politecnico di Milano (PoliMi). Besides the description of the overall re-entry campaign, this work focuses on the innovative contribution given by the Bistatic Radar for LEO survey (BIRALES), an Italian bistatic radar system managed by INAF with the support of PoliMi. Although the challenging off-nominal conditions, BIRALES was capable to acquire observation data throughout the entire campaign, providing both range, range-rate and angular track measurements to the C-SSA. The latter measurements were obtained through a novel approach based on an adaptive beamforming technique, which allows to reach a high-quality track. To obtain a re-entry epoch prediction, orbit determination results are computed starting from the last BIRALES observations without transit prediction, by also including measurements from the MFDR-MR sensor, a tracking radar managed by AM that participated in the campaign. A novel approach is also presented to estimate the ballistic coefficient in a stochastic way, and this estimation is used in both the orbit determination and the re-entry analysis. The re-entry epoch prediction computed in this way is fully consistent with the reference one provided by the U.S. Space Command. [ABSTRACT FROM AUTHOR]

Background: Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install. Methods: This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames. Results: The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations. [ABSTRACT FROM AUTHOR]

A new method is proposed for identifying ground clutter in single-polarization radar data based on the gated recurrent unit (GRU) neural network. This method needs five independent input variables related to radar reflectivity structure, which are the reflectivity at current tilt, the reflectivity at the upper tilt, the reflectivity at 3.5 km, the echo top height, and the texture of reflectivity at current tilt, respectively. The performance of the new method is compared with that of the traditional method used in the Weather Surveillance Radar 1988-Doppler system in four cases with different scenarios. The results show that the GRU method is more effective than the traditional method in capturing ground clutter, particularly in situations where ground clutter exists at two adjacent elevation angles. Furthermore, in order to assess the new method more comprehensively, 709 radar scans from Nanchang radar in July 2019 and 708 scans from Jingdezhen radar in June 2019 were collected and processed by the two methods, and the frequency map of radar reflectivity exceeding 20 dBZ was analyzed. The results indicate that the GRU method has a stronger ability than the traditional method to identify and remove ground clutter. Meanwhile, the GRU method can also preserve meteorological echoes well. [ABSTRACT FROM AUTHOR]

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity. [ABSTRACT FROM AUTHOR]

In the domain of Space Situational Awareness (SSA), the challenges related to orbit determination and catalog correlation are notably pronounced, exacerbated by data scarcity. This study introduces an initial orbit determination methodology that relies on data obtained from a single surveillance radar, with the need for fast algorithms within an operational context serving as the main design driver. The result is a linearized least-squares fitting procedure incorporating an analytically formulated approximation of the dynamics under the J 2 perturbation, valid for short-term propagation. This algorithm utilizes all available observables, including range-rate, distinguishing it from other similar methods. A significant contribution of this paper is the enhancement of estimation quality by incorporating information about the object's predicted orbital plane into the methodology, a method denoted as OPOD. The proposed methods are evaluated through a series of simulations against a classical range and angles fitting method (GTDS) to examine the effects of track length and measurement density on the quality of full state estimation, including the impact of using arcs that are too short. The OPOD methodology shows promising results throughout a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms. [ABSTRACT FROM AUTHOR]

The expansion of air traffic has led to an increase in mixed secondary surveillance radar (SSR) signal replies, which occur when multiple replies arrive at the receiver antenna simultaneously. These overlapping signals render the messages unrecoverable, leading to a loss of information. Additionally, the existing methods do not effectively address the issue of handling different types of signals with varying structures and characteristics. The authors validate the effectiveness of the disjoint component analysis (DCA) criterion for SSR signals and introduce a new DCA‐based algorithm designed to optimise the separation process. Through several simulations, the proposed algorithm demonstrates robust performance across various reception parameters. Additionally, it achieves good results when applied to real‐world data, showing its practical applicability and efficiency. [ABSTRACT FROM AUTHOR]

Drone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights. [ABSTRACT FROM AUTHOR]

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works. [ABSTRACT FROM AUTHOR]

Every night during spring and autumn, the mass movement of migratory birds redistributes bird abundances found on the ground during the day. However, the connection between the magnitude of nocturnal migration and the resulting change in diurnal abundance remains poorly quantified. If departures and landings at the same location are balanced throughout the night, we expect high bird turnover but little change in diurnal abundance (stream‐like migration). Alternatively, migrants may move simultaneously in spatial pulses, with well‐separated areas of departure and landing that cause significant changes in the abundance of birds on the ground during the day (wave‐like migration). Here, we apply a flow model to data from weather surveillance radars (WSR) to quantify the daily fluxes of nocturnally migrating birds landing and departing from the ground, characterizing the movement and stopover of birds in a comprehensive synoptic scale framework. We corroborate our results with independent observations of the diurnal abundances of birds on the ground from eBird. Furthermore, we estimate the abundance turnover, defined as the proportion of birds replaced overnight. We find that seasonal bird migration chiefly resembles a stream where bird populations on the ground are continuously replaced by new individuals. Large areas show similar magnitudes of take‐off and landing, coupled with relatively small distances flown by birds each night, resulting in little change in bird densities on the ground. We further show that WSR‐inferred landing and take‐off fluxes predict changes in eBird‐derived abundance turnover rate and turnover in species composition. We find that the daily turnover rate of birds is 13% on average but can reach up to 50% on peak migration nights. Our results highlight that WSR networks can provide real‐time information on rapidly changing bird distributions on the ground. The flow model applied to WSR data can be a valuable tool for real‐time conservation and public engagement focused on migratory birds' daytime stopovers. [ABSTRACT FROM AUTHOR]

This paper is devoted to the application of object localization and identification with information combined from a radar system and a dedicated portable/mobile electronic device equipped with a global positioning system (GPS) receiver. This device is able to provide object's (staff member, and staff vehicle) rough location and identification. Such systems are required in very restrictive security areas like airports (e.g., open-air area and apron). Currently, the outdoor area of the airport is typically protected by a surveillance system operated by security guards. Surveillance systems are composed of different sensors, video and infrared cameras, and microwave radars. The sheer number of events generated via the system can lead to fatigue among staff, potentially resulting in the omission of critical events. To address this issue, we propose an electronic system equipped with a wireless module and a GPS module. This approach enables automatic identification of objects through the fusion of data from two independent systems (GPS and radar). The radar system is capable of precisely localizing and tracking objects, while the described system is able to identify registered objects. This paper contains a description of the subsystems of a portable/mobile electronic device. The fusion of information from the proposed system (rough location and identification) with the precise location obtained from short-range radar is intended to reduce the number of false alerts in the surveillance system. [ABSTRACT FROM AUTHOR]

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

The squall line is a type of convective system that is characterized by storm cells arranged in a line or band pattern and is usually associated with disastrous weather. The identification and tracking of squall lines thus play important roles in early warning systems for meteorological disasters. Here, a clustering-based identification and tracking algorithm for squall lines is presented based on weather radar data. A clustering analysis is designed to distinguish the strong echo area and estimate the feature value, including the reflectivity value, length, width, area, endpoints, central axes, and centroid. The linearly arranged clusters are merged to improve the identification of squall line development. The three-dimensional structure and movement tracking of the squall line are obtained using the centroid and velocity of the squall lines identified in each layer. The results demonstrate that the method can effectively identify and track one or more squall lines across the radar surveillance area. The results also show that the recognition accuracy rate for the single scan elevation of this method is 95.06 %, and the false-positive rate is 3.17 %. This method improves the accuracy of squall line identification in the development stage of squall lines and still works efficiently even when high interference contamination occurs. [ABSTRACT FROM AUTHOR]

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife. [ABSTRACT FROM AUTHOR]

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered. [ABSTRACT FROM AUTHOR]

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was a year-round campaign conducted by the US Department of Energy at the Scripps Institution of Oceanography in La Jolla, CA, USA, with a focus on characterizing atmospheric processes at a coastal location. The ground-based prototype of a new Ka-, W-, and G-band (35.75, 94.88, and 238.8 GHz) profiling atmospheric radar, named CloudCube, which was developed at the Jet Propulsion Laboratory, took part in the experiment during 6 weeks in March and April 2023. This article describes the unique data sets that were obtained during the field campaign from a variety of marine clouds and light precipitation. These are, to the best of the authors' knowledge, the first observations of atmospheric clouds using simultaneous multifrequency measurements including 238.8 GHz. These data sets therefore provide an exceptional opportunity to study and analyze hydrometeors with diameters in the millimeter- and submillimeter size range that can be used to better understand cloud and precipitation structure, formation, and evolution. The data sets referenced in this article are intended to provide a complete, extensive, and high-quality collection of G-band data in the form of Doppler spectra and Doppler moments. In addition, Ka-band and W-band reflectivity and Ka-, W-, and G-band reflectivity ratio profiles are included for several cases of interest on 6 different days. The data sets can be found at https://doi.org/10.5281/zenodo.10076227 (Socuellamos et al., 2024). [ABSTRACT FROM AUTHOR]

In the last few years, many space surveillance initiatives have started to consider the problem represented by resident space object overpopulation. In particular, the European Space Surveillance and Tracking (EUSST) consortium is in charge of providing services like collision avoidance, fragmentation analysis, and re-entry, which rely on measurements obtained through ground-based sensors. BIRALES is an Italian survey radar belonging to the EUSST framework and is capable of providing measurements including Doppler shift, slant range, and angular profile. In recent years, the Music Approach for Track Estimate and Refinement (MATER) algorithm has been developed to retrieve angular tracks through an adaptive beamforming technique, guaranteeing the generation of more accurate and robust measurements with respect to the previous static beamforming approach. This work presents the design of a new data processing chain to be used by BIRALES to compute the angular track. The signal acquired by the BIRALES receiver array is down-converted and the receiver bandwidth is split into multiple channels, in order to maximize the signal-to-noise ratio of the measurements. Then, the signal passes through a detection block, where an isolation procedure creates, for each epoch, signal correlation matrices (CMs) related to the channels involved in the detection and then processes them to isolate the data stream related to a single detected source. Consequently, for each epoch and for each detected source, just the CM featuring the largest signal contribution is kept, allowing deriving the Doppler shift measurement from the channel illumination sequence. The MATER algorithm is applied to each CM stream, first estimating the signal directions of arrival, then grouping them in the observation time window, and eventually returning the target angular track. Ambiguous estimates may be present due to the configuration of the receiver array, which cause spatial aliasing phenomena. This problem can be addressed by either exploiting transit prediction (in the case of cataloged objects), or by applying tailored criteria (for uncatalogued objects). The performance of the new architecture was assessed in real operational scenarios, demonstrating the enhancement represented by the implementation of the channelization strategy, as well as the angular measurement accuracy returned by MATER, in both nominal and off-nominal scenarios. [ABSTRACT FROM AUTHOR]

The article discusses the advancements in synthetic aperture technologies, specifically synthetic aperture sonar (SAS) and synthetic aperture radar (SAR), which have revolutionized various fields such as military surveillance, environmental monitoring, and disaster management. The special issue features scientific papers focusing on optimizing collection geometry for long-range synthetic aperture sonar interferometry, implementing target recognition and classification in SAR images, model selection techniques for seafloor scattering statistics in SAS images, exploring different SAR configurations for change detection, and characterizing seafloor sediments using endfire SAS. The papers highlight innovative results and advancements in both SAS and SAR fields, showcasing the introduction of concepts like multidimensionality, machine learning, and endfire SAS to address new and traditional challenges in the respective fields. [Extracted from the article]

This study investigates the influence of different braking mechanisms on the formation of three long-period radio pulsars (PSRs J0250+5854, J2251−3711, and J0901−4046): plasma-filled magnetosphere in combination with magnetic field decay, fall-back disc, and r-mode instability. These braking mechanisms can also affect the thermal evolution of pulsars. By comparing the model-predicted values with observational data such as spin periods, period derivatives, and upper limits of the bolometric luminosity of these pulsars, we find that these three braking mechanisms can reasonably explain the spin period and the period derivative within a certain range of parameters for these sources. The model-predicted bolometric luminosity associated with magnetic field dissipation exceeds the upper limit for PSR J0901−4046 but falls below the upper limits for PSR J0250+5854 and PSR J2251−3711. The model-predicted bolometric luminosity within the fall-back disc model exceeds the observed results, whereas the luminosity within the r-mode instability falls below the observed upper limit for these three pulsars. However, a conflict arises when we consider the pulsar radio activity and the accretion phases within the fall-back disc model simultaneously. By combining data from radio and X-ray observations, along with precise measurements of surface thermal emissions, we can enhance our understanding of the braking mechanisms involved in the formation of long-period radio pulsars or constrain key model parameters. Finding more long-period pulsars in the future and conducting multiband observations will enhance our understanding of the formation and nature of long-period radio pulsars. [ABSTRACT FROM AUTHOR]

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than − 10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems. [ABSTRACT FROM AUTHOR]

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. [ABSTRACT FROM AUTHOR]

Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator's ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence "SpaceFest" 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar's utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar's ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities. [ABSTRACT FROM AUTHOR]

Many communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post‐event assessment, offering fire agencies opportunities for additional surveillance with radar, an all‐weather instrument that can image day or night. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar L‐band instrument. We test whether polarimetric radar can detect fire scars, burn severity, and different fuel types through its sensitivity to different scattering mechanisms. Polarimetric SAR products are moved into geographic information system‐friendly formats, and in lieu of available field measurements are analyzed alongside agency data showing fire perimeters, burn progression outlines, and soil burn severity. We find that the HV polarization returns and the primary scattering mechanism, quantified through the Cloude‐Pottier decomposition, are the most sensitive parameters. Higher HV values pre‐fire correspond well to areas of moderate and high soil burn severity, and the pattern of fire progression follows higher HV to some extent. Using an HV difference threshold of 1.5 dB, the Bobcat burn scar is identified at 0.70 accuracy when compared with the published fire perimeter. Alpha 1 Angle can also demonstrate sensitivity to soil burn severity pre‐ and post‐fire, showing vegetation types with increased surface scattering post‐fire, which can be used to map burn scars and track recovery by vegetation type. Plain Language Summary: Wildfires around the world lead to loss of lives, property, and environmental benefits. The increasing usage of satellite imagery to aid disaster response and monitoring offers fire agencies an opportunity for additional surveillance. Radar instruments can see through smoke, haze, and clouds during the day or night, which is especially relevant when cloud cover or weather conditions block traditional visual surveys of damage. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas were imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar, an airborne sensor with high quality measurements and detailed resolution. For these neighboring fires, we investigate the usage of radar remote sensing to detect fire scars, burn severity, and different fuel (vegetation) types. These fire characteristics are observed using a variety of polarimetric radar products. These products are analyzed alongside agency data such as burned area outlines, burn progression outlines, and burn severity. We demonstrate the advantages of using radar data sets to understand the vegetation which contributed to the fires and to monitor post‐fire recovery. Key Points: Polarimetric radar products can offer supplementary information on available fuels, past fire scars, and vegetation recoveryAlpha angle from eigenvectors is able to separate burn severity classes, while HV polarization better identifies burned from unburned areaLong‐term monitoring with a similar L‐band instrument can be achieved once the upcoming NASA‐ISRO Synthetic Aperture Radar sensor is fully operational [ABSTRACT FROM AUTHOR]

Tiny person detection based on computer vision technology is critical for maritime emergency rescue. However, humans appear very small on the vast sea surface, and this poses a huge challenge in identifying them. In this study, a single-stage tiny person detector, namely the "You only look once"-based Maritime Tiny Person detector (MTP-YOLO), is proposed for detecting maritime tiny persons. Specifically, we designed the cross-stage partial layer with two convolutions Efficient Layer Aggregation Networks (C2fELAN) by drawing on the Generalized Efficient Layer Aggregation Networks (GELAN) of the latest YOLOv9, which preserves the key features of a tiny person during the calculations. Meanwhile, in order to accurately detect tiny persons in complex backgrounds, we adopted a Multi-level Cascaded Enhanced Convolutional Block Attention Module (MCE-CBAM) to make the network attach importance to the area where the object is located. Finally, by analyzing the sensitivity of tiny objects to position and scale deviation, we proposed a new object position regression cost function called Weighted Efficient Intersection over Union (W-EIoU) Loss. We verified our proposed MTP-YOLO on the TinyPersonv2 dataset. All these results confirm that this method significantly improves model performance while maintaining a low number of parameters and can therefore be applied to maritime emergency rescue missions. [ABSTRACT FROM AUTHOR]

Long-term observations of deep convective cloud (DCC) vertical velocity and mass flux were collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon2014/5) experiment. Precipitation echoes from a surveillance weather radar near Manaus, Brazil, are tracked to identify and evaluate the isolated DCC lifecycle evolution during the dry and wet seasons. A radar wind profiler (RWP) provides precipitation and air motion profiles to estimate the vertical velocity, mass flux, and mass transport rates within overpassing DCC cores as a function of the tracked cell lifecycle stage. The average radar reflectivity factor (Z), DCC area (A), and surface rainfall rate (R) increased with DCC lifetime as convective cells were developing, reached a peak as the cells matured, and decreased thereafter as cells dissipated. As the convective cells mature, cumulative DCC properties exhibit stronger updraft behaviors with higher upward mass flux and transport rates above the melting layer (compared with initial and later lifecycle stages). In comparison, developing DCCs have the lowest Z associated with weak updrafts, as well as negative mass flux and transport rates above the melting layer. Over the DCC lifetime, the height of the maximum downward mass flux decreased, whereas the height of the maximum net mass flux increased. During the dry season, the tracked DCCs had higher Z , propagation speed, and DCC area, and were more isolated spatially compared with the wet season. Dry season DCCs exhibit higher Z , mass flux, and mass transport rate while developing, whereas wet season DCCs exhibit higher Z , mass flux, and mass transport rates at later stages. [ABSTRACT FROM AUTHOR]

We report on our discovery of the radio pulsar, PSR J2016+3711, in supernova remnant (SNR) CTB 87, with a ∼10.8σ significance of pulses, which confirms the compact nature of the X-ray point source in CTB 87. It is the first pulsar discovered in SNRs using Five-hundred-meter Aperture Spherical radio Telescope (FAST). Its integrated radio pulse profile can be well described by a single component, with a width at 50 per cent of the peak flux density of about 28.1○ and an effective width of about 32.2○. The mean flux density at 1.25 GHz is estimated to be about 15.5  |$\mu$| Jy. Combined with the non-detection of the radio pulse at lower frequencies, the radio spectral index of the pulsar is constrained to be ≲2.3. We also present the timing solution based on 28 follow-up FAST observations. Our results reveal a period of 50.81 ms, period derivative of 7.2 × 10−14 s s−1, and dispersion measure of 428 pc cm−3. The strength of the equatorial surface magnetic dipole magnetic field is inferred to be about 1.9 × 1012 G. Using the ephemeris obtained from the radio observations, we searched Fermi -LAT data for gamma-ray pulsations but detected no pulsed signal. We also searched for radio pulses with FAST towards the X-ray counterpart of the gamma-ray binary HESS J1832−093 proximate to SNR G22.7−00.2 but found no signal. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current state-of-the-art in radar technology, focusing on multiple-input multiple-output (MIMO) and beamforming techniques, to address this growing concern. It explores the challenges associated with detecting UAVs in urban settings and adverse weather conditions, where traditional radar systems often do not succeed. This paper examines the existing literature and technological advancements to understand how these methodologies can significantly boost detection capabilities under the constraints of low RCS. In particular, MIMO technology, renowned for its spatial multiplexing, and beamforming, with its directional signal enhancement, are evaluated for their efficacy in the context of UAV surveillance and defense strategies. Ultimately, a comprehensive comparison is presented, drawing on a variety of studies to illustrate the combined potential of integrating these technologies, providing the way for future developments in radar system design and UAV detection. [ABSTRACT FROM AUTHOR]

This document explores the significance of remote sensing in monitoring maritime activities and identifying vessels. It emphasizes the need for surveillance to ensure safety, security, and emergency management, given the increasing number of vessels worldwide. The document highlights the use of technologies like the Automatic Identification System (AIS) and remote sensing in situations where collaborative systems are not reliable. It also discusses the integration of data from different sensors and the application of data science techniques for a comprehensive assessment of maritime traffic. The document concludes by summarizing research papers on ship detection, tracking, and classification using various sensors and data processing techniques. [Extracted from the article]

The goal of explainable artificial intelligence (XAI) is to solve problems in a way that humans can understand how it does it. For data association there is growing demand for XAI, in which the measurement uncertainty and target (dynamic or/and measurement) model uncertainty are two fundamental problems in maneuvering target tracking in clutter. It commonly suffers of false alarms and missed detections. These situations focus on enhancing explainability, mitigating bias and creating better outcomes for all. Most the probabilistic data association (PDA) methods are weakly able, or even unable, to explain data association. To overcome these situations, the XAI components employed of two modules of Fuzzy-joint probability data association (FJDA) and Fuzzy maneuver compensator (FMC) are first established. Next, these two modules are further employed to construct maneuver tracking scheme, FJDA is then utilized to evaluate the association degree of measurements belonging to different targets and FMC plays compensation role in accordance with maneuver need. The performances of the proposed maneuver tracking scheme were compared with the PDA method and the joint probabilistic data association (JPDA) method using simulated radar surveillance data under a high cluttered environment. The numerical simulation proposed maneuver tracking scheme embedded XAI components FJDA/FCM having a remarkable improvement, due to fully utilize the useful knowledge information in the data association and reduces the impact of measurement uncertainties of the maneuvering target tracking with changing dynamics. [ABSTRACT FROM AUTHOR]

The individual subpulses of pulsars are regarded as their basic emission components, providing invaluable information for our understanding of the radio emission process in the pulsar magnetosphere. Nevertheless, subpulses are overlapped with each other along the rotation phase for most pulsars, making it difficult to study the statistical properties of subpulses. Among the pulsars observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), PSR B1916+14 has a large number of isolated well-resolved subpulses in the high-time-resolution observations, having a typical width of 0.15 ms and a high linear polarization. We find that the number distribution of subpulses contributes dominantly to the mean profile. According to the emission geometry, these emission units come from a region roughly 155 km above the polar cap in the pulsar magnetosphere, and the length-scale of the basic emission units is approximately 120 m. The deviations of the polarization position angles for these single subpulses from the standard S-shaped curve are closely related to their fractional linear and circular polarization, and the large deviations tend to come from drifting subpulses. [ABSTRACT FROM AUTHOR]

The capability of prompt response in the case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective, a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding, or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed Information and Communication Technology framework devoted to understanding the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of (i) detecting the presence of vessel targets in the input imagery, (ii) estimating the vessel types on the basis of their geometric and scatterometric features, (iii) estimating the vessel kinematics, (iv) classifying the navigation behavior of the vessel and predicting its route, and, eventually, (v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public annotated datasets. [ABSTRACT FROM AUTHOR]

Today, aviation has grown significantly in importance. However, the challenge of flight delays has become increasingly severe due to the need for safe separation between aircraft to mitigate wake turbulence effects. The primary emphasis of this investigation resides in elucidating the evolutionary attributes of wake vortices in homogeneous isotropy turbulence. The large eddy simulation (LES) method is used to scrutinize the dynamic evolution of wake vortices engendered by an A333 aircraft in the atmospheric milieu and assess its ramifications on the ARJ21 aircraft. The research endeavor commences by formulating an LES methodology for the evolution of aircraft wake vortices, integrating adaptive grid technology to reduce the necessary grid volume significantly. This approach enables the implementation of axial and non-axial grid adaptive refinement, leading to more accurate simulations of both axial and non-axial vortices. Numerical simulations are conducted using the LES approach to scrutinize three distinct rates of turbulence dissipation amidst the ambient atmospheric turbulence, and the results are juxtaposed with Lidar measurements (Wind3D 6000 LiDAR) of wake vortices acquired at Chengdu Shuangliu International Airport (CTU). Subsequently, the rolling moment of the following aircraft is calculated, and three-dimensional hazard zones are determined for the A333. It is found that during the approach phase, the wake turbulence separation minima for an ARJ21 (CAT-F) following an A333 (CAT-B) is 3.35 NM, which represents a reduction of approximately 33% compared to ICAO RECAT (Wake Turbulence Re-categorization). The findings validate the dependability of the fine-grained mesh used in the vortex core region, engendered through the adaptive grid method, which proficiently captures the Crow instability and the interconnected phenomena of vortices in the numerical examination of aircraft wake. The safety of wake encounters primarily depends on the magnitude of environmental turbulence and the development of structural instability in wake vortices. [ABSTRACT FROM AUTHOR]

This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired via X-band marine radar. The estimation capabilities of the sensor are investigated firstly on synthetic radar data. With this aim, case studies referring to sea waves interacting with a constant and a spatially varying bathymetry are both considered. Finally, the reconstruction procedure is tested by processing real data recorded at Bagnoli Bay, Naples, South Italy. The preliminary results shown here confirm the potential of the radar sensor as a tool for sea wave monitoring. [ABSTRACT FROM AUTHOR]

Wing smarting and eliminating conventional control surfaces are fundamental parameters for improving aircraft aerodynamic performance in future aviation. Twist is a well-known tool that, along with the development of morphing technology, can play a crucial role in controlling next-generation aircraft. However, wing twisting with a control approach requires many aerodynamic studies, particularly at the high aft-swept angle; this is more noticeable in the flying wing configuration. In this paper, a control strategy based on twist morphing has been evaluated aerodynamically at the Swing, a flying wing configuration. Extraction of aerodynamic coefficients and flow field on the wing have been performed using the computational fluid dynamics (CFD) method in an incompressible flight regime. To comprehensively cover the control needs, two control concepts have been introduced, called independent and nonindependent twist. Within the concept of independent twist, a co-oriented twist arrangement (Co-OTA) and counteroriented twist arrangement (Cun-OTA) are applied to the wings, which are used to produce pitching and rolling moments, respectively. The results show these control arrangements have high efficiency at low angles of attack (AoAs), but as the AOA increases, their aerodynamic performance will gradually decrease. In this regard, a significant challenge for Cun-OTA is producing the yawing moment during roll maneuvering. Attempts to solve this problem have led to the idea of a compound twist arrangement (CTA). The existence of a control arrangement to generate yawing moment is beyond the capacity of the independent twist concept. The nonindependent twist has been introduced as a solution to this issue. Conventional control surfaces have various weaknesses, one of the most important of which is the unfavorable effects on aerodynamic efficiency; the reduction of aerodynamic efficiency leads to a drop in flight endurance and an increase in fuel consumption. At a step forward, with the approach of wing smarting and morphing technology, the tasks of conventional control surfaces can be delegated to the wing itself. Therefore, it will be possible to remove the control surfaces and integrate the wing. In this article, according to the mentioned approach, an attempt was made to introduce a control strategy based on the geometric twist. In this strategy, the value and direction of the twist applied to each wing produces a specific control arrangement. Control arrangements (three types of arrangements) are involved in producing longitudinal and lateral and modified lateral moments. The results indicate that the use of twisting in aircraft control is efficient for a wide range of flight conditions, and this capability exists to be used in the next generation of aircraft. In this way of control, challenges are deliberately related to the design of the operating mechanism, safety issues, and weight. [ABSTRACT FROM AUTHOR]

Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from factors such as sensor performance and environmental conditions. To address this problem, the Gaussian process (GP)-based maximum correntropy criterion square root cubature Kalman filter algorithm (GP-MCC-SRCKF) for EOT in non-Gaussian noise environments is proposed in this paper. The proposed method utilizes a GP to model extended objects, thereby enhancing estimation accuracy. Furthermore, weighted least squares (WLS) and MCC are incorporated to construct a cost function. The proposed method considers high-order moments of estimation error and effectively handles outliers in non-Gaussian noise environments. MCC-SRCKF improves the accuracy of object state estimation in non-Gaussian noise environments while ensuring the positive definiteness and symmetry of the error covariance matrix. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter. [ABSTRACT FROM AUTHOR]

In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach. [ABSTRACT FROM AUTHOR]

The capability to design, manufacture and test Hypersonic Glide Vehicles (HGVs) has been demonstrated by a number of nations, and they are increasingly forming part of military inventories, potentially offering capabilities highly unique to this technology. This article reports the simulated Monostatic Radar Cross Section of a generic HGV in five frequency ranges, HF, VHF, UHF, L, and S‐bands associated with different radar types. Full spherical datasets of complex co‐ and cross‐polar data are synthesised so that backscatter resulting from illumination by r.f./microwave energy of linear or circular polarisation can subsequently be computed from the raw dataset. Circular polarisation is commonly employed by ground‐based Ballistic Missile Early Warning Systems and Space Object Surveillance and Identification radars to avoid polarisation mis‐match losses resulting from ionospheric Faraday rotation effects. The data was generated using Ansys' Finite Element Solver at 10, 150 and 430 MHz, with the Geometric Optics/Physical Optics based SBR+ solver employed for 1.3 and 3 GHz data. All data was produced at below the Nyquist sampling interval relevant to the target's electrical size. These datasets were then imported into a Matlab routine which extracted data over limited angular ranges associated with the likely radar line‐of‐sight in particular scenarios, typically having a standard deviation of ±10° about the direction of flight, applying either a Gaussian or Uniform sampling distribution as part of a Monte Carlo analysis. These extracted data were then used to form histograms giving the probability of sampling particular RCS values. Probability density functions and cumulative distribution functions were then fitted, to aid in the representation of statistical target fluctuations for each band and angular sampling range. The HGV exists in either the 'Rayleigh', 'resonance' or 'optical' scattering regimes, depending on its relative electrical size. The results suggest that for this target shape at HF and VHF cases a simple Swerling 0 (fluctuation invariant) approximation is adequate in most instances, whilst a Gamma distribution may be applied for UHF band cases. At L and S‐band a Beta distribution was found to provide a good fit to the available data. [ABSTRACT FROM AUTHOR]

This article discusses several aspects related to modeling the impact of offshore wind farms on maritime radar systems operating in the X and S bands. The first part of the paper focuses on theoretical models and analyses, taking into account radio shadowing and false radar echoes. Additionally, the issue of spatial modeling of wind turbines, with diffraction phenomena considered, is reviewed with the help of suitable propagation models. By relaying on a software-based implementation of the proposed model, the authors carried out a detailed simulation of the impact of wind turbines on radar systems operating in the X and S bands. The results of these simulations are presented and discussed in the second part of the article. [ABSTRACT FROM AUTHOR]

A wideband meta-material absorber with square and circular split rings that is based on a frequency selective surface of conductive ink is proposed. With over 90%absorptivity, the structure demonstrates broad absorption for the C, X, KU, and K bands, as well as polarization independent characteristics for both TE and TM, at angles of up to 45°. Research has been performed to better understand the absorption phenomenon by looking into real and imaginary permittivity, permeability, normalized impedance, and surface current density. The metamaterial absorber (MA) discussed in this study finds use in defense-related applications, such as radar surveillance, stealth technology, terrestrial and satellite communications. [ABSTRACT FROM AUTHOR]

To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar. [ABSTRACT FROM AUTHOR]

Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods. [ABSTRACT FROM AUTHOR]

Numerous satellites with electric propulsion perform long duration maneuvers during their orbit acquisition phase. This poses a challenge to space object cataloging activities if no information regarding the maneuver plan is known a priori. Various works have been devoted to maneuver detection and tracking of space objects using radar and optical surveillance data, but the special case of unknown dynamics, analogous to a maneuver spanning several days or months, has received less attention. Herein, we propose a methodology to maintain custody of uncooperative low-thrust spacecraft under the special characteristics of surveillance radar observations. Based on the stochastic hybrid systems framework, a multi-hypothesis algorithm leverages information derived from measurements and the dynamical characteristics of the object. The latter is accomplished in an efficient manner by means of a low-thrust control metric that relies on the Thrust Fourier Coefficients approximation. To improve filter robustness, the maneuver transition density is assumed to be uniform within the set of reachable states, whose outer bound is approximated via the proposed control measure. The algorithm is applied to the orbit acquisition phase of a LEO satellite using synthetic data from a simulated surveillance radar network, and its shown to be capable of maintaining custody throughout the entire three-month transfer. • Custody maintenance of uncooperative low-thrust spacecraft using radar surveillance measurements. • Definition of an efficient control metric tailored to thrust-limited transfers. • Uninformative maneuver transition densities seem to reduce biasedness and improve filter consistency. [ABSTRACT FROM AUTHOR]

This paper proposes an Synthetic Aperture Radar (SAR) imaging and detection model of multiple targets at the maritime scene. The sea surface sample is generated according to the composite rough surface theory. The SAR imaging model is constructed based on a hybrid EM calculation approach with the fast ray tracing strategy and the modified facet Small Slope Approximation (SSA) solution. Numerical simulations calculate the EM scattering and the SAR imaging of the multiple cone targets above the sea surface, with the scattering mechanisms analyzed and discussed. The SAR imaging datasets are then set up by the SAR image simulations. A modified YOLOv7 neural network with the Spatial Pyramid Pooling Fast Connected Spatial Pyramid Convolution (SPPFCSPC) module, Convolutional Block Attention Module (CBAM), modified Feature Pyramid Network (FPN) structure and extra detection head is developed. In the training process on our constructed SAR datasets, the precision rate, recall rate, mAP@0.5 and mAP@0.5:0.95 are 97.46%, 90.08%, 92.91% and 91.98%, respectively, after 300 rounds of training. The detection results show that the modified YOLOv7 has a good performance in selecting the targets out of the complex sea surface and multipath interference background. [ABSTRACT FROM AUTHOR]

The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques' degrees of suitability. [ABSTRACT FROM AUTHOR]

We report the observations of single pulse emission from the pulsar B0809+74 at 150 MHz using the Polish LOFAR station, PL-611. The three major phenomena of subpulse drifting, nulling, and mode changing associated with single pulse variations are prominently seen in these observations. The pulsar has a single-component conal profile and the single pulses are primarily in the 'normal' drift mode with periodicity (P 3) 11.1 ± 0.5 P for 96  per cent of the observing duration, while the shorter duration 'slow-drift' mode has P 3 = 15.7 ± 1.2 P. We were able to measure the phase behaviour associated with drifting from the fluctuation spectral analysis that showed identical linear phase variations across the pulse window for both modes despite their different periodic behaviour. Earlier studies reported that the transitions from the normal state to the slow-drift mode were preceded by the presence of nulling with typical durations of 5 to 10 periods. Our observations however seem to suggest that the transition to nulling follows shortly after the pulsar switches to the slow-drift mode and not at the boundary between the modes, with one instance of complete absence of nulling between mode switching. In addition, we also detected a second type of short-duration nulls not associated with the mode changing that showed quasi-periodic behaviour with periodicity PN ∼ 44 ± 7. The variety of features revealed in the single pulse sequence makes PSR B0809+74 an ideal candidate to understand the physical processes in the Partially Screened Gap dominated by non-dipolar magnetic fields. [ABSTRACT FROM AUTHOR]

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. [ABSTRACT FROM AUTHOR]