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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]

Flights are projected to reach eight billion globally by 2037, demanding airport operators manage operations effectively, including safety on the runway due to the high number of aircraft movements. One crucial issue is any foreign object, commonly known as foreign object debris (FOD), that must be detected and cleaned immediately to ensure aircraft safety when taking off, landing, and taxing. The International Civil Aircraft Organization (ICAO) reported that FOD causes 10.08% of aviation accidents. Most airports manually monitor and detect FOD, which could be more effective and dangerous. Therefore, it is important to provide FOD detection systems with proper technologies. Radar technologies are potential FOD detection techniques that offer robustness to weather fluctuation. However, some factors must be considered properly to provide an effective FOD system. This paper reviews radar technologies for FOD detection on airport runways by considering factors, including types of debris, detection coverage, mode of radars, frequencies, and attenuation. It was found that all critical factors considered contribute to the quality of detection. This paper will provide guidelines for developing FOD detection based on radar technologies regarding airport necessities and its specific environment. [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]

When electromagnetic waves propagate through the ionosphere, they deviate from the free-space propagation path because of refraction. This paper presents an analysis of the propagation paths of electromagnetic waves passing through the ionosphere over the Korean Peninsula, based on inhomogeneous 3D electron density data. On the basis of this analysis, the quantitative refraction errors are calculated considering the operating frequency, elevation angle, and variations across daily, weekly, monthly, yearly, and solar cycles. The results reveal that the refraction error caused by the ionosphere increases significantly at lower elevation angles and frequency bands than at higher elevation angles and frequency bands. Further, the refractive error is calculated according to the solar activity cycle, confirming that the refractive error increases as the solar maximum approaches. The analysis findings can help rectify refraction errors in satellite communication, space surveillance radar, and synthetic aperture radar (SAR) systems. [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]

[Objective] Due to the increasing system complexity, multielectric aircraft will experience various malfunctions, seriously affecting their safe and stable operation. However, traditional fault-detection methods are generally based on preset judgment rules and fixed detection thresholds, which are difficult to adapt to the dynamic changes in the operation status of aircraft. In addition, traditional fault-detection methods based on statistical data and expert experience lack learning ability and are, therefore, unable to detect unknown faults. Moreover, handling problems such as large data and low analysis efficiency using traditional methods is difficult, making it impossible to achieve real-time diagnosis. Therefore, there is an urgent need to develop an efficient fault-detection technology to achieve accurate and real-time fault detection in complex operating modes for multielectric aircraft. [Methods] First, a multilevel feature extraction module with ResNet as the core unit is built to obtain multilevel feature expressions about faults, improving the real-time performance of fault diagnosis. In addition, by constructing a multiscale feature extraction module with a BiLSTM network as the core unit, long-term-dependent features during fault evolution can be obtained, thereby ensuring fault diagnosis accuracy. Second, a Bayesian optimization algorithm-based hyperparameter optimization method with fault detection accuracy as the objective function is proposed, which helps improve the learning effect of fault features and further enhance the fault detection performance. Finally, a multielectricaircraft fault-detection experiment is designed, and the detection performance under different fault modes is analyzed. [Results] In the hyperparameter optimization method, the accuracy of the fault detection model on the training set is selected as the objective function, and an acquisition function is used to maximize the objective-function solution, thereby obtaining the optimal hyperparameter combination that affects the detection performance in the model. In a comparative experiment of fault detection, to verify the effectiveness of the proposed method, comparative experiments were conducted with commonly used methods, including ResNet-BiLSTM models without Bayesian optimization, CNNs, and BiLSTM. The results show that the proposed Bayesian optimization-based method can achieve effective fault-detection results in various fault modes and that compared with those of existing methods, the accuracy and real-time performance of this method are better.[Conclusions] The proposed method can effectively detect and classify different faults in various operation modes, which helps achieve accurate analysis of faults for multielectric aircraft and provides very powerful technical support for flight safety. [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]

Aims: To describe the development and report the first‐stage validation of a digital version of the digit symbol substitution test (DSST), for assessment of cognitive function in older people with diabetes. Materials and Methods: A multidisciplinary team of experts was convened to conceptualize and build a digital version of the DSST and develop a machine‐learning (ML) algorithm to analyse the inputs. One hundred individuals with type 2 diabetes (aged ≥ 60 years) were invited to participate in a one‐time meeting in which both the digital and the pencil‐and‐paper (P&P) versions of the DSST were administered. Information pertaining to demographics, laboratory measurements, and diabetes indices was collected. The correlation between the digital and P&P versions of the test was determined. Additionally, as part of the validation process, the performance of the digital version in people with and without known risk factors for cognitive impairment was analysed. Results: The ML model yielded an overall accuracy of 89.1%. A strong correlation was found between the P&P and digital versions (r = 0.76, p < 0.001) of the DSST, as well as between the ML model and the manual reading of the digital DSST (r = 0.99, p < 0.001). Conclusions: This study describes the development of and provides first‐stage validation data for a newly developed digital cognitive assessment tool that may be used for screening and surveillance of cognitive function in older people with diabetes. More studies are needed to further validate this tool, especially when self‐administered and in different clinical settings. [ABSTRACT FROM AUTHOR]

The rapid development of aircraft in recent years has attracted more attention, especially the issue of safe flight for aircraft. Some existing angle detection sensors are difficult to adapt to the development speed of the current aircraft due to the expensive price and large size, so there is an immediate need for new technology that can suit the current aircraft. Here, a self‐powered digital angle sensor that is compact, simple, and lightweight is introduced. This sensor eliminates the need for an external power supply and demonstrates highly sensitive signal detection at the volt level when the variable structure of the aircraft is working. The sensor is designed with a thickness of 7.3 mm, a volume of 16.6 cm3, and a weight of 9.85 g. Its lightweight and compact structure allows for seamless integration into aircraft without causing significant impact. The sensor not only provides real‐time monitoring the aircraft's variable structure deflection states, including deflection direction and angle but also alerts operators to any anomalies in deflection. Its small size and self‐powered capability make it an ideal solution for aircraft applications, enhancing safety and efficiency during flight. [ABSTRACT FROM AUTHOR]

A novel compact microwave metamaterial absorber structure with a wide bandwidth is proposed. The absorber structure is designed on an FR4 substrate with a 2.4 mm thickness. The metamaterial absorber unit cell design consists of a combination of W and S-shaped structures in which splits are incorporated to have the effect of a split ring resonator. The proposed unit cell structure is designed to have a wide absorption band, which includes the X and Ku frequency bands. The optimized dimension of the unit cell is 0.071 λ0 × 0.71 λ0 where λ0 is the wavelength of the center frequency of the absorption band. The WS-shaped structures are oriented to absorb TE and TM waves for a wide range of polarization angles. The simulation results of the unit cell have shown absorption above 95% from 10.48 to 13.28 GHz. In addition, the absorber has an efficiency above 92% for incidence angles from 0° to 60° for fixed polarization. The unit cell structure and array are fabricated, and measurements are performed. The simulation and measured results have shown good agreement. The proposed metamaterial can be used in defense and radar surveillance applications. [ABSTRACT FROM AUTHOR]

Social media and digital platforms have become essential tools for the new generation of youth activists. However, these tools subject youth to both new (and old) forms of surveillance and control. Drawing on in-depth interviews and social media walkthroughs with 61 youth activists, I examine hybrid tactics that these youth employ to resist surveillance and other forms of digitally mediated control as they participate in politics and social movements. I show that even in democracies like the United States and Canada, for individuals along intersecting axes of marginalization (e.g. race, gender), public political acts do not capture the full range of young people's political repertoires. Young people, especially those from marginalized groups, adopt hidden, under-the-radar tactics in response to pressures of social, state, and corporate surveillance. I develop the concept of "digital infrapolitics" referring to the ways in which digital politics and activism go below the radar under surveillance and control. [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]

The amount of energy available in a system constrains large-scale patterns of abundance. Here, we test the role of temperature and net primary productivity as drivers of flying insect abundance using a novel continental-scale data source: weather surveillance radar. We use the United States NEXRAD weather radar network to generate a near-daily dataset of insect flight activity across a gradient of temperature and productivity. Insect flight activity was positively correlated with mean annual temperature, explaining 38% of variation across sites. By contrast, net primary productivity did not explain additional variation. Grassland, forest and arid-xeric shrubland biomes differed in their insect flight activity, with the greatest abundance in subtropical and temperate grasslands. The relationship between insect flight abundance and temperature varied across biome types. In arid-xeric shrublands and in forest biomes the temperature–abundance relationship was indirectly (through net primary productivity) or directly (in the form of precipitation) mediated by water availability. These results suggest that temperature constraints on metabolism, development, or flight activity shape macroecological patterns in ectotherm abundance. Assessing the drivers of continental-scale patterns in insect abundance and their variation across biomes is particularly important to predict insect community response to warming conditions. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [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]

Recent breakthroughs in modern surveillance systems combined with stealth and jamming technologies have led to the development of high-directional and high-gain antennas. This paper presents a compact-sized antipodal Vivaldi antenna (AVA) consisting of an elliptical shaped radiating flare with rectangular slots at the flare edges for modern surveillance systems. The proposed antenna provides improvements in the antenna characteristics, including a higher front-to-back ratio, increased main lobe gain, reduction in the side lobe level (SLL), and lower frequency limit. The results reveal that at 20 GHz, an improved gain of 3.8 dB and a front-to-back ratio of more than 40 dB have been realised. Additionally, in contrast to the antenna without edge slots, the impedance bandwidth, |S11| < −10 dB of 15 GHz, and a reduction in the lower operating frequency by 36.7% have also been achieved. The prototype antenna is developed and tested to verify with simulation results. The proposed antenna with the miniaturised structure may be suitable for surveillance applications in modern radar systems because of its ultrawide fractional bandwidth, high directive gain, and reduced SLL. [ABSTRACT FROM AUTHOR]

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify groundmoving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints. [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]

The streamwise vorticity current (SVC) has been shown in recent research to have a role in the tornadogenesis process in some supercells. Although field experiments have succeeded in observing the feature using mobile radars, limited knowledge exists on how/if SVCs can be observed using operational radars. To explore this possibility, simple radar emulation software was used to create simulated Weather Surveillance Radar 1988 Doppler (WSR-88D) radar images of an SVC in a high-resolution numerical simulation of a supercell thunderstorm. This proof-of-concept approach shows that SVCs likely can be observed in an operational setting under idealized circumstances. Two analysis times from the simulation are used to compare the signatures of both a weak and a strong SVC. This study suggests that results are highly contingent on radar viewing angle, and quality observations of the SVC may only be possible when a storm is within 45 km of the radar site. In Plan Position Indicator (PPI) scans, SVCs are found to be associated with inflow winds that penetrate more rearward with height downstream of the mesocyclone. Range Height Indicator (RHI) reconstructions show a reflectivity billow that resembles a density current head containing a Kelvin-Helmholtz (KH) billow, as well as a radial velocity couplet co-located with the SVC. A vertical shear product is introduced to visualize low-level vertical wind shear in the vicinity of the SVC to facilitate identification of the feature without interrogation of multiple elevation angles of radial velocity data. [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]

Congestion free mobility has become nearly impossible in most of the metropolitan cities of India especially during peak hours. The understanding of factors inducing congestion demands huge amount of data pertaining to urban traffic. The developed countries have adopted different kinds of automatic data collection systems such as loop detectors, surveillance cameras and radars for the data collection of road traffic condition. In developing countries like India, the collection and monitoring of data related to movement of traffic stream are mostly manual, very time consuming and expensive. In India, Intelligent Transport System (ITS) has been implemented to Mysore City public transport in the year 2012. This study makes use of Global Positioning System (GPS) data of Mysore ITS. The major objective of the present study is to evaluate the congestion on urban roads using public transit GPS data with the help of visualization techniques. Spatio-temporal visualization-based analysis has been carried out to evaluate the traffic congestion patterns of urban roads. Initially, the comparison of traffic states on urban street and arterial road has been carried out. Later, the difference in congestion patterns before and after the operation of grade separator and the impact of route diversion on the congestion patterns have been evaluated. This study shows that public transit GPS data can be a potential data source to evaluate the traffic state or congestion, especially when there are limited sources of traffic data. [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]

One of the most important components for air traffic is the runways, which can act as a bottleneck for the arrival flow and cause delays. At Tokyo International Airport, the arrival flow is managed according to the characteristics of air traffic flow from two directions to two runways. This study focused on the effects of changing landing runways on flight times and taxiing times. A queuing model was derived for each runway from the statistical results of surveillance radar data during the busy hours. The flight times depending on changing the landing runway were estimated by the queuing models. To show the effectiveness of each landing runway change, a performance index averaging the times was minimized under the constraint of the number of landing runway changes. The results revealed that some landing runway changes effectively reduced times as intended, while others increased times or had little effect. [ABSTRACT FROM AUTHOR]

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]