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Many of the requirements for autonomous sensing of moving vehicles are the same as those for automatic cruise control radars, whose development is now sufficiently mature to be on offer to the consumer car market. This paper investigates the suitability of these radars for use on an autonomous vehicle, and describes a capability assessment of two specific radars for such a task.

In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

The article deals with the recognition methods of surveillance 3D radar types in airborne electronic intelligence stations based on the use of priori information about the radar scanning parameters are considered. The subject is the process of intelligence information processing in aerial ELINT assets. The purpose of the article is to state the methods of surveillance 3D radar types in airborne electronic intelligence stations based on the use of a priori knowledge of radar scanning parameters. The height finders, V-beam radars, vertical-frequency scanning radars, phased-array radars from air defense systems scan the space in the vertical plane to measure the elevation angle (height). The scanning methods and parameters of such radars, namely, pulse width and repetition period, operating frequency range and scanning space parameters have significant differences that’s why they to be used in solving the radar type recognition problem. It’s important to note that this data can be obtained from open source information. The article covers the theoretical aspects of the recognition algorithm, which are based on the overlapping of the distribution densities of parameters of the likelihood ratio. In accordance with the conclusions of the pattern recognition theory, the solution of the problem is based on the calculation of the logarithm of the likelihood ratio for each pair of radio emission sources. It is proposed to solve the recognition problem in two stages. At the first stage (preliminary), there is a division of the observed sources of radio emission into two classes. The first class includes all three types of height finders (RRV-13, RRV-16, RRV-17) and radar 1L117. Their characteristics are the double frequency of illumination of the ELINT station during the period of rotation (nodding) of the radar antenna when scanning the space. The second class includes all the latest types of radar. At the first stage, it is proposed to perform recognition by comparing illumination periods using the developed algorithms. In order to distinguish the second-class radar, the frequency range, pulse width and repetition period, as well as the scan period of the space (antenna rotation) should be used as a feature. Radar types 19Zh6 or 35D6 differ from others by the presence of almost simultaneous radiation at 4 fixed operating frequencies and a consistent four-stroke measurement of the pulse repetition period at the time illumination by the main beam.

The investigators have researched all the major radar types over the past decades and so far, have selected the bistatic and monostatic radar output along with the dynamic range of the radar system. We focus on specific differences in the relationship between the budget noise, transmission and reception (TR), the ratio of signal to noise (SNR) is last described detailed when limitation is addressed, then the potential improvement is deduced. The key differences are the radiological differences between bistatic and monostatic radar systems. Over the past decades, the researchers have studied the two main radar types, and the bistatic and monostatic radar performance and the dynamic range of the radar system have been chosen between them so far. we focus on the fundamental differences he fundamental differences between the bistatic and monostatic radar system are the relationship between budget noise, transmitting and receiving (TR) pair loss, the signal to noise ratio (SNR) is last defined in detail where limitation is discussed, then the possible improvement is deduced.

This paper presents a survey of the state-of-the-art advances in human vital signs detection using radar sensors, their integration and coexistence with communication systems, and their issues in spectrum sharing. The focus of this survey is to review the detection, monitoring, and tracking of vital signs, specifically the respiration rate and heartbeat rate over the recent five years. It is observed that in line with technological advancements, a multitude of radar types operating in diverse frequency spectra have been introduced with different hardware implementations, considering various detection scenarios, and applying multiple signal processing algorithms. The aim of these researches varies, from enhancing the detection accuracy, improving the processing speed, reducing the power consumption, simplifying the hardware used, lowering implementation costs, and the combinations of them. Besides that, this review also focuses on literature aimed at increasing the detection accuracy and reducing the processing time using FPGAs, prior to benchmarking them against other processing platforms. Finally, a perspective on the future of human vital signs detection using radar sensors concludes this review.

In this paper, we present a method for the identification of different multifunction radar emitter types. It is based on Long Short-Term Memory recurrent neural networks and a previously published hierarchical modelling approach. This approach maps radar pulses to different levels of symbols which can be regarded as parts of a radar language. We evaluate our method with an example emitter that can make use of three different resource management techniques. The results show that it is possible to distinguish between radar types that mainly use the same emission parameters but differ in the resource management method.

The classical radar range equation has been around since the beginning of radar. While there are various forms of the equation for different radar types, it has not commonly been expressed in a graphical form until now. Leonardo has developed a novel means of expressing the equation in a form that provides insight into radar performance, particularly with regard to detection of targets with non-uniform radar signatures. In essence, the graphical format splits the contribution to detection into components for the radar parameters and those for the target; both components are scaled precisely in range.

Passive radar systems are used in the military for intelligence gathering, threat detection and as a support to electronic attack systems. Therefore, radar emitter recognition is a crucial task of reconnaissance systems for accurately identification of hostile threats. However, this problem is challenging due to the complicated noisy electromagnetic environment as well as the increasing complexity of modern radar signals. In this paper, we introduce a novel deep neural network-based scheme, named ParallelNet for the recognition of different radar types. In our approach, I/Q samples and radar pulse features extracted from received wideband signal are inputs of two parallel sub-neural networks. The outputs of sub-networks are subsequently combined to deduce the classification result. We realize extensive simulations to show that ParallelNet achieves an outstanding performance in terms of recognition accuracy and robustness in severely noisy conditions.

Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2-3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95 s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.

This paper presents a technological demonstrator with traffic applications, which carries out three typical intelligent transportation system (ITS) tasks: traffic flow measurement, speed estimation, and vehicles classification. The proposed system is a frequency-modulated continuous wave radar working at 24 GHz, with side-looking installation. Two signal-processing modes, 1-D and 2-D, are introduced as detection scheme. Unlike other existing commercial systems, this demonstrator makes use of phase information from target echoes in order to perform clutter cancellation and target speed measurement. To this end, two different speed-estimating methods are proposed and compared by means of experimental results obtained from a complex scenario.

[EN] Multiple-input multiple-output (MIMO) radars have been shown to improve target detection for surveillance applications thanks to their proven high-performance properties. In this paper, the design, implementation, and results of a complete 3-D imaging frequency-modulated continuous-wave MIMO radar demonstrator are presented. The radar sensor working frequency range spans between 16 and 17 GHz, and the proposed solution is based on a 24-transmitter and 24-receiver MIMO radar architecture, implemented by timedivision multiplexing of the transmit signals. A modular approach based on conventional low-cost printed circuit boards is used for the transmit and receive systems. Using digital beamforming algorithms and radar processing techniques on the received signals, a high-resolution 3-D sensing of the range, azimuth, and elevation can be calculated. With the current antenna configuration, an angular resolution of 2.9° can be reached. Furthermore, by taking advantage of the 1-GHz bandwidth of the system, a range resolution of 0.5 m is achieved. The radio-frequency front-end, digital system and radar signal processing units are here presented. The medium-range surveillance potential and the high-resolution capabilities of the MIMO radar are proved with results in the form of radar images captured from the field measurements.

A new 94-GHz frequency-modulated continuous wave (FMCW) Doppler radar–radiometer system [Jülich Observatory for Cloud Evolution (JOYCE) Radar–94 GHz (JOYRAD-94)] is presented that is suitable for long-term continuous observations of cloud and precipitation processes. New features of the system include an optimally beam-matched radar–radiometer; a vertical resolution of up to 5 m with sensitivities down to −62 dBZ at 100-m distance; adjustable measurement configurations within the vertical column to account for different observational requirements; an automatic regulation of the transmitter power to avoid receiver saturation; and a high-powered blowing system that prevents hydrometeors from adhering to the radome. JOYRAD-94 has been calibrated with an uncertainty of 0.5 dB that was assessed by observing a metal sphere in the radar’s far field and by comparing radar reflectivities to a collocated 35-GHz radar. The calibrations of the radar receiver and the radiometric receiver are performed via a two-point calibration with liquid nitrogen. The passive channel at 89 GHz is particularly useful for deriving an estimate of the liquid water path (LWP). The developed retrieval shows that the LWP can be retrieved with an RMS uncertainty (not including potential calibration offsets) of about ±15 g m−2 when constraining the integrated water vapor from an external source with an uncertainty of ±2 kg m−2. Finally, a dealiasing method [dual-radar dealiasing method (DRDM)] for FMCW Doppler spectra is introduced that combines measurements of two collocated radars with different measurement setups. The DRDM ensures high range resolution with a wide unambiguous Doppler velocity range.

A compact-array high-frequency surface wave radar equipped with two crossed-loop/monopole receiving antennas has been established for vessel detection. Using two compact antennas of the same design, this system can obtain two extremely similar sets of radar range-Doppler spectra over the same period. To detect vessel targets efficiently, the spectra of two antennas are enhanced by performing a principle component analysis. A wavelet-based approach is then applied to suppress clutter and reduce noise. The signal-to-noise ratios and signal-to-clutter ratios of the echoes are thus improved. Finally, an adaptive threshold is used to extract targets. The real radar data detection results are compared with Automatic Identification System data as well as those from the conventional ordered-statistic constant false alarm rate method. The feasibility and the validity of method proposed here are thus demonstrated.

Ionospheric sounding is a technique that provides real-time data on high-frequency ionospheric-dependent radio propagation. This letter presents a Universal Software Radio Peripheral-based ionospheric sounding radar, which relies on a basic system consisting of a synchronized transmitter and receiver. The radar has the advantages of miniaturization, modularization, low power, and low cost. The three most significant features of the radar system are that it is software-defined and universal platform-based and that it has low transmitting power. This novel software-defined vertical-incidence radar system can probe the ionosphere and obtain real-time plasma parameters according to the simulation. Ionograms that directly express probe results are generated by MATLAB after data processing and simulation. Successful development of such an ionospheric sounding software radar will allow universalization and miniaturization of an ionosonde radar system. This letter introduces the implementation of the novel ionospheric sounding radar.

A stepped-frequency radar that allows for adaptability in the radiated spectrum while maintaining high-resolution radar imagery has been developed. The spectrally agile frequency-incrementing reconfigurable (SAFIRE) radar system is a vehicle-mounted, ground-penetrating radar that is capable of producing high-resolution radar imagery for the detection of obscured targets (either buried or concealed surface targets). It can be easily transitioned between forward- and side-looking orientations. The SAFIRE system is capable of precisely excising subbands within its operating bandwidth, thus making the system “spectrally agile.” It is also highly reconfigurable thereby allowing for on-the-fly adjustment of many of the system parameters. The spectrally agile and reconfigurable aspects of the SAFIRE radar together with its enhanced IF processing scheme represent a novel contribution to the state of the art. This paper discusses the system design, implementation, and performance characteristics, and also presents preliminary high-resolution imagery.

Bitstreams, square wave digital signals, enable flexible radar implementations in modern digital technology. By using bitstreams in place of analog sinusoidal waveforms, we can realize continuous-wave (CW), stepped-frequency CW, frequency-modulated CW, or even pseudo random noise-sequence and pulsed radars, all with a single bit of amplitude resolution. The building blocks are a programable waveform generator, a sweep threshold quantizer, digital delay, and a digital XOR gate as a mixer. This gives us a novel, almost fully digital (requiring only a comparator) system, as previously proposed and which is extended here. The flexibility of the transmitter allows for easy switching between waveforms and the bitstream signal can be processed with single-bit digital gates. Single-bit signals allows for exploration of novel continuous time non-clocked digital implementations to maximize speed and energy efficiency. Mixing frequencies with a digital XOR gate creates harmonics, which are explored for multiple solutions utilizing digital delay. Analytical as well as simulation results are presented. Initial measurements from a 90 nm CMOS chip is provided for the transmitter and the full system, proving the feasibility of a digital future in radar.

A new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations and real weather radar data with RFI are used to perform algorithm comparisons. Results are consistent with theoretical considerations and show the 2D RFI filter to be a promising addition to the signal processing arsenal against interference with weather radars. Increased computational burden is the only drawback relative to filters currently used by operational systems.

Radar is an indispensable technology in areas of defense, air traffic control (ATC), and weather surveillance. The usual goal of defense and air traffic control radars is to detect and track individual targets; in contrast, the "target" of weather radars may stretch hundreds of kilometers. The meteorology, defense, and ATC communities typically publish research in different venues, and each community has a vernacular and characteristic ways of approaching problems. The goals of this article are twofold. Its primary goal is to summarize typical weather radar systems and define associated jargon in terms more familiar to other radar communities. The second goal is to provide a detailed discussion of the physical scattering phenomenology exploited specifically in radar meteorology. Furthermore, while there are a number of operational weather radars, we focus throughout much of this article on the most common weather radar, Weather Surveillance Radar 1988 Doppler (WSR-88D), as a canonical example, because much of the current research and phenomenological analysis are based on its operational parameters.

Short-range radar systems are noncontact devices that are attracting much attention from the RF/microwave community. In particular, they are being applied in healthcare contexts, ranging from hospital to indoor and outdoor environments, e.g., for monitoring of vital signs and/or tracking of human activities. This article reviews the standard architectures for short-range biomedical radars and presents recently proposed hybrid schemes, which combine different operation modes, leading to performance improvement. Specifically, hybridizations associated with Doppler, frequency-modulated continuous-wave, and tone-ranging-based schemes are provided from theoretical and simulation viewpoints. Some experiments on vital-sign monitoring and human-aware localization are also reported.

When Privates George Elliott and Jo s eph Lockard discovered a large fleet of airplanes approaching Pearl Harbor, Hawai'i, on the morning of 7 December 1941, they were operating a new U.S. Army Signal Corps radar that used the same antenna for transmitting and receiving RF energy [1]. RF engineers had to quickly develop a variety of new techniques to keep the multikilowatt transmitter signals from degrading or damaging the sensitive receivers in those early systems. This past December marked the 75th anniversary of that event. Tying in with the exhibit of related hardware at the IEEE Microwave Theory and Techniques Society's (MTT-S) 2017 International Microwave Symposium in Honolulu this June, the goal of this article is to highlight some of the components and circuits used for transmit?receive (T/R) switching in U.S. radars from the pre-World War II era through the following few decades.

Weather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries. However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations. To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur. The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador. Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars. Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities. Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based and satelliteborne measurements. To demonstrate practical applications, a map of areas frequently affected by intense rainfall is presented, based on a time series of one radar that has been in operation since 2002. Such information is of vital importance to, for example, infrastructure management because rain-driven landslides are a major issue for road maintenance and safety throughout Ecuador. The presented case study of exceptionally strong rain events during the recent El Niño in March 2015 highlights the system’s practicality in weather forecasting related to disaster management. For the first time, RadarNet-Sur warrants a spatial-explicit observation of El Niño-related heavy precipitation in a transect from the coast to the highlands in a spatial resolution of 500 m.

With more weather radars relying on low-power solid-state transmitters, pulse compression has become a necessary tool for achieving the sensitivity and range resolution that are typically required for weather observations. While pulse compression is well understood in the context of point-target radar applications, the design of pulse compression waveforms for weather radars is challenging because requirements for these types of systems traditionally assume the use of high-power transmitters and short conventional pulses. In this work, Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern requirements are used to illustrate how suitable requirements can be formulated for the radar range weighting function (RWF), which is determined by the transmitted waveform and any range-time signal processing. These new requirements set bounds on the RWF range sidelobes, which are unavoidable with pulse compression waveforms. Whereas nonlinear frequency modulation schemes are effective at reducing RWF sidelobes, they usually require a larger transmission bandwidth, which is a precious commodity. An optimization framework is proposed to obtain minimum-bandwidth pulse compression waveforms that meet the new RWF requirements while taking into account the effects of any range-time signal processing. Whereas pulse compression is used to meet sensitivity and range-resolution requirements, range-time signal processing may be needed to meet data-quality and/or update-time requirements. The optimization framework is tailored for three processing scenarios and corresponding pulse compression waveforms are produced for each. Simulations of weather data are used to illustrate the performance of these waveforms.

Regulators, aware of the significant underutilization of spectrum reserved for radar operation, are starting to open these bands for sharing with commercial services. In this paper, we provide the signal processing techniques necessary to apply temporal sharing to reduce radar exclusion zones and increase spectral efficiency. Our approach directly extends to the fairly common scenario of multiple radars operating at relatively close distance in the same frequency and allows a secondary user to transmit without exceeding a stipulated level of interference at any radar. We require only that radars behave periodically; our secondary users apply adaptive sensing to track radar behavior in real-time without a priori information. To accomplish this, we introduce a pulse deinterleaving mechanism to separate multiple radar emissions in real-time, with no batch or offline processing. We show that our approach to temporal sharing is applicable to static or low mobility sharing scenarios, where the interference channel displays quasi-periodic features.

Increasing demand for wireless communications services within the spectrum bands that are most conducive to operating cellular and mobile broadband systems is aggravating frequency congestion within the electromagnetic spectrum. Primary-secondary spectrum sharing can be exploited to alleviate spectrum congestion by allowing secondary systems to access opportunistically the allotted frequency band of a primary system. In this study, the feasibility of spectrum sharing between air traffic control (ATC) radar and long-term evolution (LTE) wireless communications systems is investigated through waveform-level simulations and link budget analyses. Numerical results show that with moderate frequency and range separation, spectrum sharing between a scanning ATC radar and a single LTE cell in L-band is possible, though both systems are ultimately subject to quantified performance degradations due to the impact each has on the other.

Software-defined radio (SDR) platforms represent a compelling solution for low-cost, flexible, dual-polarized radar systems. However, the phase coherency requirements of a dual-polarized radar system between the transmit ports and between the receive ports as well as between transmission and reception, are difficult to attain in popular SDRs. In this paper, we provide high-level overviews of SDR radar systems, dual-polarization radars, and system phase calibration procedures found in literature. The method adopted to achieve coherency involves a manual calibration procedure, which is applied to four dual-polarized radar system configurations designed around commercial off-the-shelf SDR platforms. The implemented, calibrated designs were exercised in a laboratory setting to determine the coherence performance of the SDR-based radar architectures in characterizing a fixed target’s full-polarization scattering matrix.

In an effort to study and characterize scattering mechanisms of debris particles in tornadoes, a numerical polarimetric radar emulator was developed. This paper is primarily motivated by attempts to explain radar observations near tornadoes. One such observation is the regions of negative differential reflectivity, which have been found near tornadoes but they are yet to be explained physically. There are hypotheses that suggest common debris alignment and/or dominant scattering from objects with high radar-cross-section (RCS) values that cause negative $Z_{DR}$ , but they are extremely challenging to verify due to the inherent danger near the vicinity of tornadoes. It is, however, possible to numerically construct the scenes through representative simulations to verify the plausible causes. This serves as our primary motivation to develop the radar emulator. The novel aspects of this paper are the realistic trajectory derivation, which is based on a physical air-drag model, and the representative diversity of RCS contributions from each debris object, developed through realistic polarimetric RCS modeling and anechoic chamber measurements.

The analysis and correction of the bias occurring in weather radar polarimetric measurements is a challenging problem. Polarization coupling due to the cross-polar radiation pattern of the radar’s antenna is known to be responsible for errors in the estimation of the polarimetric covariance matrix, and consequently in the hydrometeor classification and quantification, either when using the ATSR or the SHV method. An alternative method for Doppler and polarimetric measurements based on transmitting three different polarizations (3-PolD) has been proven to provide accurate polarimetric covariance matrix estimates without making any hypothesis about the target polarimetric response or its Doppler spectrum. This method does not reduce the Doppler capabilities or the unambiguous range of the radar despite alternately transmitting 3-PolD. These characteristics have encouraged evaluating the polarimetric parameter biases due to cross-polar radiation when this method is used. Biases are calculated considering reflector antenna systems as well as phased-array antenna systems. The results show that this method may guarantee a tolerable bias level even with a poor co- to cross-polar antenna pattern ratio.

Future weather radar systems will need to provide rapid updates within a flexible multifunctional overall radar network. This naturally leads to the use of electronically scanned phased array antennas. However, the traditional multifaced planar antenna approaches suffer from having radiation patterns that are variant in both beam shape and polarization as a function of electronic scan angle; even with practically challenging angle-dependent polarization correction, this places limitations on how accurately weather can be measured. A cylindrical array with commutated beams, on the other hand, can theoretically provide patterns that are invariant with respect to azimuth scanning with very pure polarizations. This paper summarizes recent measurements of the cylindrical polarimetric phased array radar demonstrator, a system designed to explore the benefits and limitations of a cylindrical array approach to these future weather radar applications.

This paper deals with a symbiotic radar, defined as a passive radar that is an integral part of a communication network. The symbiotic radar is integrated with an IEEE 802.22 Wireless Regional Area Network and linked with the base station. It can work as a purely passive radar or, and this is the novelty in the system, can use the base station to suggest the best customer premise equipment that should be scheduled for transmission to improve tracking performance. This paper defines a cognitive passive tracking algorithm that exploits the feedback information contained in the target state prediction to improve the performance while preserving the communication capabilities of the complete network.

Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.

Today the main applications of ground based passive coherent location (PCL) systems are in the domain of air traffic surveillance–air target detection, tracking, and localization. Interest in using PCL radars in these applications is expressed primarily by military users, mainly because of the great advantages of silent and covert operation that passive radars provide. In this regard, passive radars benefit from the fact that they operate by using emissions already present (e.g. FM radio, DAB, DVB-T, GSM, Wi-Fi, etc.) rather than their own. Other advantages of passive radars relate to the possibilities of stealth target detection and gap filling in areas not covered by active radars. Another huge advantage of the passive radar lies in the fact that there is no need for requirements for spectrum allocation or powerful transmitters. Consequently, as the aforementioned points make passive radars great representatives of “green” technology, one can install them in “sanctuaries”–specific or specialized places such as high-density urban areas, sites located in the vicinities of hospitals, and/or other places where electromagnetic emissions are limited by law because of the risk of interference or simply through the public's fear of electromagnetic fields. Also, it is worth mentioning that PCL systems can be cheaper than active ones as the passive radar does not have its own transmitter and typically consists of a simple radio receiver with commercial off-the-shelf (COTS) hardware, and uses a commercially available PC as a computation.

The possibility of obtaining cross-range profiles of aerial targets is contemplated in this paper by applying inverse synthetic aperture radar (ISAR) techniques against FM-based passive radar data. The considered application is shown to pose a number of limitations, mostly related to the very long coherent processing interval required to attain a reasonable cross-range resolution. To mitigate such limitations, a suitable two-stage ISAR processing scheme is proposed for the estimation of the target motion induced phase history and its compensation. The results are shown against experimental data sets collected by a very high frequency (VHF) band passive radar receiver developed at Sapienza University of Rome. This allows to demonstrate the effectiveness of the proposed approach that provides the FM-based passive radar with advanced capabilities for air traffic surveillance applications.

Traffic speed is a crucial input for real-time traffic management applications. Operating agencies typically deploy their own sensors to collect the measurements, e.g., loop detectors. Recently, Sp...

In modern warfare, as a long distance detection equipment, a reconnaissance radar is crucial to monitor the sensitive regions and the intelligence of airborne targets. It is important for the opponent to identify the tactical information about the enemy’s reconnaissance radar. It has a great influence on the war. Scanning cycle of the monitoring radar is an important parameter for the counterreconnaissance of the hostile radar source in the electronic countermeasure. Since the pulse Doppler (PD) radar is one of the most widely used type in modern reconnaissance radar domains, this paper focuses on scanning cycle estimation of the reconnaissance radar (PD radar) using a single unmanned aerial vehicle (UAV). We propose an effective method to reconstruct the main-beam pattern (MBP) curve of the radar antenna based on the norm approximation algorithm, and then the reconstructed MBP curve of the radar antenna is exploited to estimate the scanning cycle of the reconnaissance radar. By hovering the UAV at the same place, the scanning cycle of the reconnaissance radar can be estimated according to the reconstructed MBP curve. In the simulation section, we check the validity and robustness of the proposed method through the performance comparison with the Cramer-Rao lower bound.

The aim of the radar systems is to collect information about their surroundings. In many scenarios besides static targets there are numerous moving objects with very different characteristics, such as extent, movement behavior or micro-Doppler spread. It would be most desirable to have algorithms that extract all information on static and moving object automatically, without a system operator. In this paper, we present measurements conducted with a commercially available high-resolution multi-channel linear frequency-modulated continuous-wave radar and algorithms that do not only produce radar images but a description of the scenario on a higher level. After conventional spectrum estimation and thresholding, we present a clustering stage that combines individual detections and generates representations of each target individually. This stage is followed by a Kalman filter based multi-target tracking block. The tracker allows us to follow each target and collect its properties over time. With this method of jointly estimating tracks and characteristics of each individual target in a scenario, inputs for classifiers can be generated. Which, in turn, will be able to generate information that could be used for driver assistance or alarm trigger systems.

This thesis illustrates the necessity and design process of performance improvement for radar detection by using novel CFAR detectors. A qualitative discussion of the need for and applications of conventional CFAR detectors is presented first. Typical applications are given as examples of where the radar system may be utilized. A closer look under the Swerling 2 assumption is presented as well. The second portion of the thesis generally introduce the underlying electromagnetic theories that radar detection is built upon. The radar range equation, radar cross section, noise in systems, and vehicular applications are explained. The portion focuses on the radar detection. MASTER OF ENGINEERING (EEE)

This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m–1 km and 5 min) but are less affected by attenuation.Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant.

Radar waveform diversity has received considerable attention in recent years due to increasing spectral congestion and the burgeoning capabilities of digital waveform generation. The promise of waveform diversity is far greater utilization of available degrees of freedom to enhance sensing performance and to even facilitate new operating modes. This tutorial provides an overview of this very broad topic, from the basic principles upon which it is founded to the myriad different areas being explored in research for practical sensing applications.

A survey of radar systems used in the medical field is presented. First, medical applications of radars are described, and some emerging research fields are highlighted. Then, medical radars are analyzed in terms of block diagrams and behavioral equations and some implementations are shown as examples. A section is dedicated to the radiating structures used in these radars. Finally, human safety and environmental impact issues are addressed. The most investigated medical applications of radars are breast tumor diagnostics and remote monitoring of cardiorespiratory activity. New fields of interest are physiological liquid detection, and the monitoring of artery walls and vocal cord movements. Among the various topologies, continuous wave (CW) radars have been proven to yield the highest range resolution that is limited only by the system noise while the resolution of ultra wideband (UWB) and frequency modulated continuous wave (FMCW) radars is also related to the used frequency bandwidth. Concerning the maximum range, UWB radars have the best performance due to their ability to operate in the presence of environmental clutter. As for the radiating structures, planar antennas are preferred for diagnostic applications, due to their small dimensions and good matching when placed in contact with the human body. Radar systems for remote monitoring, instead, are designed by using high gain antennas and taking into account the complex radar cross section (RCS) of the body.

Radar technology has been evolving towards higher resolution, high-precision detection instruments with an ever-increasing list of functionalities. One of the areas, which have very good potential for combining the benefits of these developments, is multifunction radar systems. These systems perform multiple functions simultaneously, including surveillance, tracking, confirmation of false alarm, backscanning, clutter, and interference cancellation, which are traditionally performed by dedicated individual radars. With the introduction of these new radar requirements, future radar systems will need to work with wider frequency bands than traditional radar systems.

Discriminating between meteorological and nonmeteorological radar returns is necessary for a number of radar applications, including hydrometeor classification, quantitative precipitation estimation (QPE), and the computation of specific differential phase KDP. The algorithm proposed, MetSignal, uses polarimetric radar data and is simple by design, allowing users to adjust its performance based on the location’s specific needs. The MetSignal algorithm is a fuzzy logic technique with a few postprocessing rules and has been selected for implementation on the WSR-88D network in the United States.

This letter aims to show the potential of using polarimetric parameters to distinguish between large birds and unmanned aerial vehicles (UAVs) of comparable size in the context of a modern long-range air defense radar. Time is a critical resource in such systems, and techniques for robust noncooperative target recognition not relying on spatial resolution or long dwell times are highly desired. Furthermore, methods less dependent on target micromotion are, in many cases, required. Methods exploiting polarimetric features are shown to have potential in both cases. An experiment in S-band shows that a simple nearest-neighbor classifier can achieve good separation between UAVs and birds both with and without detectable micromotion based on a set of polarimetric parameters alone.

On phased-array radars, scanning is done by stepping the beam from one direction to the next direction and dwelling long enough at each direction to achieve acceptable errors of estimates. Combining data from the three directions is suggested to obtain superresolution similar to that available on the national network of weather radar (Weather Surveillance Radar-1988 Doppler or WSR-88D). Spectral analysis of such data is addressed, and it is demonstrated that the Doppler spectra of simply concatenated time series have very strong sidebands due to the discontinuity of the signals from the three beam positions. This artifact degrades the performance of the spectral clutter filters and other methods that rely on spectral processing to enhance the weather signal. Special adjustments of the signals at each range location before concatenating (splicing) are proposed to mitigate the effects of discontinuities in time and thus improve clutter filtering. The adjustment is such that the total information contained in the signal can be preserved in subsequent processing. Spectral quality of the concatenated signals is quantified via results from simulations. Samples of spectra obtained with the National Weather Radar Testbed are presented to substantiate the predictions. A ground clutter detector/filter accepted by the National Weather Service is applied to the conditioned time series data, and the ensuing fields of reflectivity factor and Doppler velocity are compared to the fields from which clutter had not been removed.

In this letter, pioneering experimental results of passive synthetic aperture radar (SAR) imaging are presented. The classical active SAR radar operates in monostatic geometry. The SAR sensor presented in this letter is a passive radar utilizing commercial Digital Video Broadcasting—Terrestrial transmitters as illuminators of opportunity. It works in a bistatic configuration, where the receiver is placed on a moving platform and the transmitter is placed on the ground and is stationary. The imaged scenes are stationary surfaces on Earth such as agriculture or urban areas, buildings, etc. In this letter, pioneering results of signal processing verified by a measurement campaign are presented. In the experiment, two synchronized passive radar receivers were mounted on a small airborne platform. The main goal of the presented experiment was to verify the possibility of ground imaging using passive SAR technology and validate previously presented theoretical results.

The pose of a rotational symmetry target in a wideband radar is the angle between its symmetry axis and the radar line of sight. It is a required parameter for target absolute attitude measurement using the bistatic radar measuring system. In this letter, a 3-D electromagnetic model (3-D EM model)-based method is proposed for estimating the target pose from fully polarimetric wideband radar measurements. The 3-D EM model is established offline based on the target geometric structure. Scattering features at different target poses can be accurately predicted by this model. In the proposed algorithm, a synthetic high-resolution range profile (HRRP), which is proved to be only related to the target pose, is first generated from the sum of echoes in radar copolarization channels. Then, by matching this synthetic HRRP with the synthetic HRRPs recovered by the 3-D EM model, the target pose is obtained. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in an anechoic chamber verify the validity of the method.

The influence of intelligence gathering inaccuracy, especially the estimate error of radar kinematic parameter, on the performance of deceptive jamming against synthetic aperture radar (SAR) is studied in this paper, filling a knowledge vacancy in the field of SAR electronic countermeasures. For the first time, an equivalence relationship between non-ideal jamming and ground moving target is derived and verified in raw phase history data domain and radar image domain. A novel and elegant way to quantify the defocus and displacement of false target in radar image is proposed based on the equivalence relationship. Work presented in this paper can guide the design of jammer’s intelligence gathering subsystem and optimize jamming configuration, from the view of jammer. The results also avail electronic counter-countermeasures, from the view of radar.

This article summarized processing approaches and presented an experimental analysis of the levels of situational awareness relevant to different sensing architectures for non-cooperative sense and avoid, based on standalone radar, standalone EO, and radar/EO data fusion, respectively.

A 3-D inverse synthetic aperture radar (ISAR) image can provide target’s information in the range, azimuth, and height dimensions. Since the height resolution is related to the variant observing angles, the existing 3-D ISAR imaging methods are highly dependent on the radar’s hardware complexity (two or more sensors). In this paper, we propose a novel 3-D shipborne ISAR imaging method, which utilizes the maneuvering movement of one single shipborne radar to obtain the height resolution, and hence, the restriction on the hardware complexity is relaxed. Moreover, a novel cubic chirplet decomposition algorithm based on Radon transform is proposed to estimate the signal parameters, and additional data from narrow-band tracking radar and shipborne attitude indicator is used for image scaling and distortion elimination. Therefore, the high-quality 3-D ISAR image can be achieved. Simulation results verify the effectiveness of the proposed methods.