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It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 p ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance.

Sensitivity and performance of homodyne radar are notably improved if the phase noise terms of transmit and receive signals are correlated and canceled out by the receiver. However, in a bistatic setup with separate oscillators, this correlation is inherently not given. To achieve a good carrier phase and phase noise coherency, it is required to transmit a radio frequency (RF) reference, e.g., by using high-quality RF cables, optical fibers, or wireless point-to-point links. All these options are complex, costly, and prone to distortions and signal delay variations. Sharing a clock signal is also barely helpful since even small distortions of this reference are converted to notable uncorrelated phase noise in the radar signal synthesizers. In this article, a novel full-duplex system concept and signal processing scheme is presented that solves the above-mentioned problems completely. This concept allows for coherent measurements within a bistatic radar setup, even if the two radar stations are only loosely coupled employing a simple clock-rate adjustment. By theory and experimental results with bistatic frequency-modulated continuous-wave radar, it is shown that our concept allows for bistatic measurements with the same sensitivity and performance that is usually only known from homodyne radar systems. To the authors’ best knowledge, this is the first time that phase noise coherent measurements in a bistatic multiple-input multiple-output radar setup are shown. This concept paves the way for advanced netted radar setups, e.g., in automotive radar and applications with large apertures or baselines or multiperspective coherent tomographic measurements.

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has an excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide-frequency band with a narrow bandwidth receiver. However, due to the use of different frequency pulses, RSF waveform is incompatible with the conventional moving-target-detection (MTD) technique, which makes clutter suppression a difficult problem for RSF radar and limits its application. In this article, the effect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signal-to-clutter ratio on target Doppler channel by $N$ (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression algorithm, which is based on designing clutter suppression filters with two-dimensional property to obtain the clear high-range-resolution profile of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment.

To perform 3-D inverse synthetic aperture radar (ISAR) imaging through interferometric processing, all ISAR images should be correctly registered such that the same scatterer appears at the same position along both the range and Doppler directions. However, this condition generally does not hold in most situations because different radar view angles introduce additional angular motion errors that must be compensated. Hence, we herein propose a new ISAR registration method based on joint-channel phase difference (JC-PD) operations. From the result of the JC-PD function, JC-PD profiles are obtained, in which the locations and phases of the maximum peak contain constant and time-varying angular motions, respectively. Using our proposed method, correct ISAR registrations can be achieved in both the range and Doppler directions. In particular, compared to conventional Doppler registration methods, time-varying angular motions are more accurately compensated without the problem of cross-term phase interference. Through simulations and experiments, we verified that the proposed method yielded accurate ISAR registration results; hence, an excellent 3-D target reconstruction through interferometric ISAR imaging was demonstrated.

We propose a new compressive sensing scheme that enables fully polarimetric radar imaging from a subset of channel measurements. Previous sparse recovery methods for polarimetric radar have not modeled channel coupling. We recognize that mixing of information through coupling is key to dropping a channel. Our method complements fast and slow time sampling and may reduce hardware and communications costs. This paper provides the signal model and examples of successful four-channel recovery when only three are measured.

Measuring the vibrating states of an artificial structure is an important approach to monitor the stability of the structure. However, the existing radar vibration measurement methods do not have enough azimuth resolution for measuring the vibrations in a relatively large scene, which contains multiple buildings or a large building. For such application scene, this paper proposes a vibration measurement method based on multiple-input multiple-output imaging radar system, which can achieve high azimuth resolution. Features of the proposed method are threefold: first of all, by utilizing the ability of quickly acquiring imaging data, the proposed method can achieve vibration measurement for the entire area simultaneously; second, in order to detect the positions of vibrating objects and decrease the time of vibration parameter estimation part, this paper proposes vibration similarity index to quantify the similarity between the detected signal and ideal vibration signal; at last, due to the limitations of the hardware performance, this paper adopts multiple signal classification least-squares estimation method to estimate the vibrating frequency and amplitude. To evaluate the performance of the proposed method, ideal point target simulation and vibrating calibrator experiment have been conducted, and the results show that the positions and the vibrating parameters of the vibrating objects fit well with the reference values. In addition, car experiment and bridge experiment have been carried out to verify the ability of the proposed method to measure the vibration of real artificial structures, which cannot be seen as point targets.

We consider range estimation and range-Doppler imaging using signed measurements of a radar system. Known time-varying thresholds are investigated for taking the signed measurements through one-bit sampling, and are compared with unknown dithering with known probability density function. The maximum likelihood (ML) approach is considered for signal parameter estimation. Since the ML algorithm is computationally prohibitive, a relaxation-based approach, referred to as the One-Bit RELAX algorithm, is used for signal parameter estimation. The conventional RELAX algorithm proposed for high-resolution sampling is also considered for comparison purposes. Moreover, a model-order selection tool, namely the Bayesian information criterion, is used to determine the number of scatterers within the scene of interest. Both numerical and experimental examples are provided to demonstrate the performance of the proposed approaches.

In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed.

The backscattered signals of objects under spinning motion or with rotating parts provide very rich information that can be used for classification tasks, parameter extraction, etc. Obtaining such information from noncooperative objects with an unknown target-aspect is often a complicated task with a monostatic configuration. A multistatic radar on the other hand, can exploit the spatial diversity to extract the information from the time–frequency representations obtained from multiple aspect-angles. In this paper, we propose a tomographic approach for characterizing spinning objects in terms of their shape, size, and rotation parameters using a narrow-band multistatic radar. A two-dimensional image is reconstructed after a full rotation period using tomographic methods that allows not only to estimate the shape of the target but also the rotation parameters and the dimensions of the object. This is done very efficiently by combining the tomographic images from different aspect-angles on the transformed log-polar space, instead of the time–frequency representations. Simulations and measurements were conducted for the proof of concept. The measurement results with a simple target and a continuous wave K-band radar show errors below 3 $^{\circ }$ for the orientation estimation and below 5% for the estimation of the object's diameter.

Detection and localization in urban environments is a very recent radar problem. In this paper, we investigate the possibility of detecting and locating targets not in direct line of sight (NLOS) areas with a single portable radar by exploiting multipath returns. We propose two algorithms, which handle the information provided by multipath returns in different ways to detect and estimate the NLOS target position. We also present an original method to select the number of paths to take into account in the algorithms in order to maximize detection probabilities. Numerical results show good efficiency of the proposed algorithms for problems of both detection and localization. We show that applying these algorithms improves detection performance compared to a classic matched filter in a typical urban scenario. Experimental results on a real dataset allow us to validate our multipath model in urban environments, and in particular to show that it is possible to retrieve the target location even with rough knowledge of the scene geometry.

Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data, but also take advantage of the polarization diversity and the aspect angle dependence information from multichannel SAR data. First, the traditional sparse representation-based classification is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Finally, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the data set we consider. Extensive experimental results on a high-fidelity electromagnetic simulated data set and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.

Analysis of the attitude and geometry of space targets with the inverse synthetic aperture radar (ISAR) technique is a significant and difficult task. Most of the existing methods hardly consider the radar observation geometry in the determination of target attitude. This paper proposes a novel approach to estimating the three-dimensional attitude and reconstructing typical component geometry of space targets from an ISAR image sequence. The approach bridges range-Doppler images and target attitude parameters with the accommodation of target trajectory information and the ISAR geometric projection model. By exploring the shape feature within the ISAR sequence, the target attitude and the rectangular component size are estimated through solving an optimization with prior shape constraints. Comparative experiments illustrate the advantages of the proposed method in both feature association and reconstruction feasibility. Moreover, considering practical circumstances, a further analysis is made of the robustness of the proposed algorithm after the attitude estimation experiment.

The radar echoes of space debris sometimes have drawbacks of inadequate pulse samples, self-occlusion, and data missing, while the state-of-art radar imaging algorithms become unproductive. To tackle this problem, this paper proposes a novel method for three-dimensional (3-D) imaging of space debris. This iterative algorithm terminates automatically and can recover the intensity of space debris radar cross section, making it practical in real-world applications. Unlike the 3-D imaging methods based on multistatic inverse synthetic aperture radar, the proposed method is applicable to monostatic ISAR.

The ability of multiple-input multiple-output (MIMO) radar to provide enhanced performance has been verified in both theoretical and practical level. Through applying orthogonal waveforms, this type of system can realize far more observation channels than the actual number of transmit and receive (T/R) elements. Generally, the azimuth one-dimensional (1-D) generalized ambiguity function (GAF) and resolution of a MIMO radar system are analyzed by pattern theory, which can only analyze the GAF along the equidistant curve to the radar's center. However, the two-dimensional (2-D) GAF of wideband MIMO radar system shows that its sidelobes are not along the equidistant curve accurately due to the coupling effect between the range-dimension and the azimuth one. Obviously, the traditional pattern theory becomes invalid in this case. To tackle this problem, the 2-D GAF and the spatial spectrum of far-field targets are derived in this paper. By analyzing the 2-D GAF, existence of three individual directions of sidelobes is revealed and it is proved that every sidelobes is along a straight line in the pseudopolar coordinate system. Furthermore, a method to calculate the 1-D GAF and resolution in the arbitrary direction is proposed. Finally, a simulation and two experiments have been carried out to validate the theoretic results.

For nonuniformly rotating target in the low signal-to-noise ratio (SNR) environment, the inverse synthetic aperture radar (ISAR) imaging is a challenging task due to the Doppler spread induced by time-varying rotation. In this paper, an effective modified bilinear parameter estimation algorithm is proposed and applied to reconstruct the ISAR image of nonuniformly rotating targets with low SNR. First, azimuth echoes of a range cell are modeled as multicomponent cubic phase signals (multi-CPSs) after range alignment and phase adjustment. Then, to estimate the parameter of the CPS (the chirp rate and the quadratic chirp rate, which are identified as causes of the target image defocus), a novel integrated parametric cubic phase function (IPCPF) and a reversing Wigner–Ville distribution processing are developed. Compared to other four representative algorithms, the bilinearity of the IPCPF guarantees a higher antinoise performance and a better suppression on cross terms. Moreover, by utilizing the nonuniform fast Fourier transform and the generalized scaled Fourier transform, the brute-force searching is eliminated and the computational cost is reduced. Finally, a new cross-range scaling method based on a regression analysis is proposed for the ISAR imaging. With the synthetic data and the real radar data, several simulation examples and ISAR imaging results demonstrate the effectiveness and the superiority of the proposed ISAR imaging algorithm.

A novel mixed mode radar coincidence imaging (MMRCI) system, which combines the coherent radar detection and incoherent radar coincidence imaging function during the radar coincidence imaging (RCI) process, is presented in this paper. The target location function of the MMRCI system gives the target position information synchronously with imaging tests, and this makes the MMRCI work independently with low target position estimation error. In the MMRCI system, the resolution of target distance estimation is proportional to the whole bandwidth assisted by the double-frequency linear frequency modulation (DFLFM) signal when the occupied bandwidth of the DFLFM signal is less than one-tenth of the whole bandwidth. The target position estimation error caused by the target motion can be reduced effectively so that the image quality of the moving target can be guaranteed in a longer detection time. The RCI is reconstructed using the incoherent part of the transmitting signal. A general relationship between the spatial correlation of the random radiation field in the imaging plane and the deployments of linear random source is analyzed in the paper. The imaging efficiency is ensured through the grading imaging method based on the imaging requirement. The effectiveness of the MMRCI method is validated via a set of simulations and experiments, including superresolution RCI of both stationary and moving targets in various scenes using the convex optimization algorithms.

Multiple-input multiple-output (MIMO) radar is an enabling technique for high-resolution imaging, which is especially useful for near-field electromagnetic scattering diagnosis of complex targets. Among others, high sidelobes and radar cross section (RCS) calibration uncertainty are the major challenges for such applications, due to array nonuniformity, imperfect channels, and antenna pattern tapering. These shortcomings prevent a MIMO radar from obtaining high-quality images with enough dynamic range and RCS accuracy. In this paper, we develop a complete solution for these problems. A novel adaptive weighting technique is proposed, where the complex weights are optimized for exact amplitude and phase error calibration of a MIMO array and for azimuth sidelobe reduction. A MIMO filtered backprojection algorithm is developed for image formation with improved RCS calibration accuracy, where propagation path-loss, antenna pattern tapering, and phase distortion due to the near-field spherical wave front are compensated. Both indoor and outdoor field test results are presented to show the high-quality images obtained using the proposed techniques, demonstrating the applicability of a MIMO radar for diagnostic RCS imaging of complex targets.

Studies have demonstrated the usefulness of micro-Doppler signatures for classifying dynamic radar targets such as humans, helicopters, and wind turbines. However, these classification works are based on the assumption that the propagation channel consists of only a single moving target. When multiple targets move simultaneously in the channel, the micro-Dopplers, in their radar backscatter, superimpose thereby distorting the signatures. In this paper, we propose a method to detect multiple targets that move simultaneously in the propagation channel. We first model the micro-Doppler radar signatures of different movers using dictionary learning techniques. Then, we use a sparse coding algorithm to separate the aggregate radar backscatter signal from multiple targets into their individual components. We demonstrate that the disaggregated signals are useful for accurately detecting multiple targets.

In this paper, the feasibility of a bistatic passive system for maritime surveillance and marine navigation is considered based on the communication satellite constellation Inmarsat as a source of illuminating signals. A power budget analysis is detailed for Inmarsat signals, considering both the reference and the radar channels. The effect of sea clutter is also examined. It is shown that relatively poor range resolution can be improved if the available communication channels are combined to improve the bandwidth, a process that also improves the sensitivity. For the first time, detection and preliminary bistatic range-Doppler tracking are presented for a representative maritime target.

Distributed inverse synthetic aperture radar (ISAR) exploits the data acquired by multiple radar sensors carried by multiple platforms working in formation to increase the cross range resolution with respect to the value achievable by single platform systems. In this frame, the paper addresses the problem of the estimation of the ship dynamics, i.e., yaw, pitch, and roll rotation motions, exploiting the signals collected by such multiplatform radar imaging systems providing angular diversity in order to enable the focusing of the distributed ISAR images. Specifically, in this work a multi-angle formation of sensors is considered and the corresponding maximum likelihood estimator and Cramer Rao bound are derived. Then, a new Doppler matching based technique is proposed as a suboptimal approach exploiting only the linear component of the phase of the received signals. The performance analysis proves the effectiveness of the proposed techniques to separately estimate the horizontal, radial, and vertical components of the rotation vector, therefore making possible both the focusing and accurate cross-range scaling of the distributed ISAR products (as well as of the low-resolution ISAR images regarding the different sensors) and, as a further advantage, providing knowledge of the orientation of the corresponding image projection planes. The analysis of experimental multisensor datasets confirms the feasibility of the proposed techniques.

Recent spotlight synthetic aperture radar analyses predict the two-dimensional range migration signature smears induced by targets with arbitrary motion in the ground plane. These investigations were limited to a constant-velocity radar motion with level flight path. The current correspondence removes this constraint by including the radar trajectory ascent angle.

Through-the-wall radar (TWR) imaging is an emerging technology that enables detection and localization of targets behind walls. In practical operations, TWR sensing faces several technical difficulties including strong wall clutter and missing data measurements. This paper proposes a sparse Bayesian learning (SBL) approach for wall-clutter mitigation and scene reconstruction from compressed data measurements. In the proposed approach, SBL is used to model both the intraantenna signal sparsity and interantenna signal correlation for estimating the antenna signals jointly. Here, the Bayesian framework provides a learning paradigm for sharing measurements among spatial positions, leading to accurate and stable antenna signal estimation. Furthermore, the task of wall-clutter mitigation is formulated as a probabilistic inference problem, where the wall-clutter subspace and its dimension are learned automatically using the mechanism of automatic relevant determination. Automatic discrimination between targets and clutter allows an effective target image formation, which is performed using Bayesian approximation. Experimental results with both real and simulated TWR data demonstrate the effectiveness of the SBL approach in indoor target detection and localization.

The use of satellite signals for passive radar detection has become quite attractive in the recent years. This paper focuses on the detection and the shadow inverse forward scatter radar (SISAR) imaging of aircrafts in the passive forward scatter radar (FSR) system using global navigation satellite system (GNSS) satellites as illuminators of opportunity. The signal model derived from GNSS tracking loop is given and its Doppler character is analyzed. Then, by comparing GNSS FSR and ground-based FSR, the SISAR signal model and the imaging method for GNSS FSR are constructed. Experiments using airliners as targets were carried out, in which forward scattering signals of both GPS satellites and BeiDou satellites were captured. The experimental results verified the correctness of the given model as well as the effectiveness of the novel imaging method.

This paper proposes a new scheme for improving the classification performance of inverse synthetic aperture radar (ISAR) images. The proposed scheme utilizes the trace transform to gather abundant information from an ISAR image, regardless of the spatial distribution of the target response in the ISAR image. In simulations using ISAR images with various spatial distributions, the proposed scheme substantially improved the classification performance compared with existing methods, which are highly vulnerable to spatial variation.

Noise radars possess several advantageous properties, including low probability of detection and identification, especially when working in continuous-wave mode. One of the main drawbacks of such radars is the occurrence of the masking effect, when a weak target echo is masked by the sidelobes of a strong target echo. The sidelobes of the ambiguity function emerge on the level of the time–bandwidth product below the main peak and are spread in the entire range–Doppler plane. While most approaches to mitigate the masking effect focus on the processing of the received signal, it is possible to move the computation burden to the waveform design phase. This paper describes a filter-based method of creating noise-like waveforms that have very low sidelobes in the area of certain range and Doppler shifts. Both theoretical analysis and measurement results are presented and compared with the lattice filter method of masking effect cancellation.

An original 3-D radar imaging system is presented for radar cross section (RCS) analysis, i.e., to identify and characterize the radar backscattering components of an object. Based on a 3-D spherical experimental setup, where the residual echo signal is more efficiently reduced in the useful zone, it is especially adapted to deal with low-RCS analysis. Due to a roll rotation, the electric field direction varies concentrically while the scattered data are collected. To overcome this issue, a specific 3-D radar imaging algorithm is developed. Based on fast regularization inversion, more precisely the minimum norm least squares solution, it manages to determine, from a single pass collection, three huge 3-D scatterer maps at once, which correspond to HH, VV, and HV polarizations at emission and reception. The algorithm is applied successfully to real X-band datasets collected in the accurate 3-D spherical experimental layout, from a metallic cone with patches and an arrow shape. It is compared with the conventional 3-D polar format algorithm where the scatterer information is irretrievably mixed-up.

Missing samples within synthetic aperture radar data result in image distortions. For coherent data products, such as coherent change detection and interferometric processing, the image distortion can be devastating to these second-order products, resulting in missed detections, and inaccurate height maps. Previous approaches to repair the coherent data products focus upon reconstructing the missing data samples. This paper demonstrates that reconstruction is not necessary to restore the quality of the coherent data products.

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.

Video synthetic aperture radar (video-SAR) is a land-imaging mode where a sequence of images is continuously formed when the radar platform either flies by or circles the scene. In this paper, the fast backprojection (FBP) algorithm is introduced for video-SAR image formation. It avoids unnecessary duplication of processing for the overlapping parts between consecutive video frames and achieves O(N2 log N) complexity through a recursive procedure. To reduce the processing complexity in video-SAR system, the scene is partitioned into the general region (GR) and the region of interest (ROI). In different regions, different aperture lengths are used. The proposed method allows a direct trade between processing speed and focused quality for the GR, meanwhile reserving particular details in the ROI. The effectiveness is validated both for a simulated scene and for X-band SAR measurements from the Gotcha data set.

Tracking low-elevation targets over a smooth surface is challenging because of the multipath effect. The symmetrical difference pattern monopulse is proposed for low-angle tracking with array radar. At first, the optimal symmetrical difference pattern is designed. The proposed monopulse is implemented using symmetrical sum and difference patterns by digital beam forming. The monopulse ratio derived from the symmetrical sum and difference patterns is not affected by the multipath effect. Then, the performance of the symmetrical difference pattern monopulse is evaluated by simulations in terms of the signal-to-noise ratio, target elevation, and complex reflection coefficient. Field data sets acquired from a VHF array radar are used for validation. It is found that the measurement error is still less than 1/30th of a beamwidth when the target elevation is less than a quarter of a beamwidth. The proposed monopulse scheme is suitable for VHF array radar, for which the multipath effect is serious.

This paper explores the use of radar odometry for Global Positioning System–denied navigation. The range progression from arbitrary ground-based point scatterers is used to estimate an unmanned aerial vehicle’s relative motion. In high clutter environments, the recursive-RANSAC algorithm provides robust and efficient feature identification, data association, and tracking. The tracked feature range measurements are integrated with inertial measurement unit measurements in an extended Kalman filter. Real flight data from a cluttered environment are used to validate the results.

Performance of space–time adaptive processing (STAP) with adaptive matched filter detection for maritime surveillance is assessed using simulated maritime surface targets embedded in real radar sea clutter data and compared with conventional pulse-Doppler processing. Pre-Doppler and post-Doppler suboptimal STAP are examined, with pulse repetition interval (PRI)-staggered post-Doppler shown to provide best overall detection and constant false alarm rate performance. A two-component clutter model fit is used to explain variations of clutter characteristics and detector performance with Doppler frequency.

Coherent multiple-input multiple-output (MIMO) radar is capable of forming a specific transmit beam pattern favorable for radar operation, but the transmitted waveforms from different antenna elements need to be orthogonal for coherent beamforming. In this work, an innovative approach is developed for MIMO radar waveform design in space-time domain, rendering MIMO radar able to satisfy transmit beamforming constraint in space-domain as well as the waveform orthogonality requirement in time domain. The approach is validated through simulations.

This paper proposes a method on high-frequency vibration compensation of helicopter-borne synthetic aperture radar (SAR) in terahertz band. The SAR signal model with platform vibration is established based on the mechanism of a helicopter's vibration. Then successive Doppler keystone transform (SDKT) is used to correct the range cell migration induced by the vibration. Finally, the analytical formula of the platform vibration phase error is given as well as its compensation to achieve a focused image.

Bistatic inverse synthetic aperture radar (ISAR) is an efficient geometric configuration of ISAR to image targets traveling along radar transmitter's line of sight, but it has the problem of defocusing and distortion because of the time-varying bistatic angle. In this paper, we derive the defocusing term and the distortion term by expanding the bistatic angle using first-order Taylor expansion. In addition, necessary constraints to neglect the defocusing term are provided via point spread function analysis, and a novel method to eliminate distortion based on linked scatterers is proposed. Simulation results validate our theoretical analysis and show the effectiveness of our method.

We propose a fast method of ultrawideband (UWB) radar imaging that can be applied to a moving target, having in mind such application as concealed weapon detection. We demonstrate the performance of the proposed method using simulations and measurements with static and moving targets. We also compare the computational complexity of the proposed method with that of a conventional method to clarify the feasibility of applying the proposed method to the intended real-time systems.

In sky-wave over-the-horizon radar (OTHR), it is quite difficult to handle the issues of target location (ground range) and height (altitude) estimation due to their joint effect on group range. This work addresses the joint estimation of target location and height by the means of multipath propagation of OTHR signals and structure of a two-dimensional (2D) array. Usually, the multipath signal results from ionosphere propagation in OTHR and can be produced in multi-input–multi-output (MIMO) radar by transmitting signals of various carrier frequencies. A 2D array provides the potential of elevation resolution, which is related to ground and slant ranges. By the multi-quasi-parabolic (MQP) ionospheric model, we formulate the multipath propagation and signal model for the OTHR with a target at location r and height h. Moreover, the joint maximum-likelihood estimates (MLEs) of r and h are derived, and the joint Fisher information matrix (FIM) is calculated. With the so-obtained FIM, the estimability is analyzed; that is: r and h are estimable if and only if either a multipath signal or 2D array is available. The joint Cramer-Rao bound (CRB) is computed and discussed for accuracy improvement. Additionally, the estimability is also extended to the joint estimation of target location, height, and velocity.

A binary phase shift keying, space time coding approach to digital beamforming radar is considered for low latency estimation of range, velocity, and bearing angles of scatterers. Scattering information from all angles within the field of view is gathered during a single range/velocity acquisition and subsequent processing produces a range/velocity matrix for each beam direction. This coded aperture radar (CAR) approach provides range, velocity, and bearing angle estimates with low latency and requires only a single transceiver. These advantages are accompanied by a decrease in SNR as compared to conventional phased array and digital beamforming radars due to the fact that CAR receives less total energy. Furthermore, obtaining a sufficient number of measurements within an acquisition period to provide unambiguous estimation for a large number of bins may be impractical. Utilizing a reduced measurement set gives rise to "residual ambiguity," whose level is shown to be proportional to total received energy.

An ultrawideband (UWB) multistatic radar system, typically composed of one transmitter and several receivers, is a promising solution for tracking intruders moving inside a surveillance area. In this paper, the Hough transform is used to find the time-of-arrival (TOA) curves of the targets in the scan versus propagation time image for impulse-radio UWB multistatic radars. In order to parametrize these curves, which associate target TOAs over scan time, a straight-line constant-velocity mobility model is assumed for the target, providing a reasonable computational complexity and data storage requirement. The performance of our approach is verified by experimental measurements for a single target scenario in an indoor environment, where a dense clutter is observed. We show that the target TOA points can be well discriminated from false alarms and associated over scan time, forming the target TOA curves. These TOA curves can be estimated with a good accuracy, even though the target is missed at some points. In practice, before applying our TOA association algorithm, we have to remove the unwanted clutter. Because the conventional empty-room method generates too many false alarms due to shadowing effects in indoor environments, we also develop a modified empty-room clutter removal technique and we show that it can considerably reduce the number of false alarms.

Micro-Doppler radar signals can be used to separate moving human targets from stationary clutter and also to identify and classify human movements. Traditional micro-Doppler radar, which uses a single-sensor, monostatic system, suffers from the drawback that only the radial component of the micro-Doppler signal will be observed by the radar operator. This reduces the sensitivity of human-activity recognition if the movements are not directly toward or away from the line of sight to the radar antenna. In this paper, we propose overcoming this limitation through the use of a multistatic sensor, which consists of two bistatic micro-Doppler sensors. Through using multiple sensors, the orientation of oscillating targets with respect to the radar line of sight can be inferred, thereby providing additional information to the radar operator. In addition, we conclude that multiple sensors can enhance the classification of micro-Doppler signatures.

Synthetic aperture radar (SAR) is a form of remote sensing where coherent radar echoes transmitted from a moving platform are processed to form an image of a scene, usually on the ground. There are several algorithms that have been developed with varying levels of complexity and accuracy. In applications with large scene size requirements, the choice of image formation algorithm is important. Exact imaging algorithms like the back-projection algorithm (BPA) can form large images without errors, but they are computationally expensive. Another well-known algorithm is the polar format algorithm (PFA), which is significantly faster than BPA, but it uses approximations that cause image errors in large scenes. In this paper, we evaluate the scene size limitations of the PFA in terms of image defocus. This is caused by residual quadratic phase errors that arise due to approximations in the algorithm. We derive this residual quadratic phase error using a Taylor series expansion in the slow time dimension. Then, we derive simplified expressions for image defocus for two flight paths: circular and linear. We also use the Taylor series expansion to derive accurate corrections for image distortion caused by PFA. These distortion corrections are used in conjunction with the residual quadratic phase errors to derive accurate scene size limitations that are notably different from the circular regions of focus determined in earlier works.

This paper develops an odometry algorithm for unmanned air vehicles (UAVs) based solely on radar measurements. The radar return from stationary ground-based scatterers is used to create a range-compressed image, which can be used to estimate the relative motion of the UAV. The Hough transform is used to extract scatterer features from the range-compressed image and to solve the data association problem. The estimated relative motion of the aircraft based on radar return is then fused with inertial measurement unit data in an extended Kalman filter. Radar data collected during actual flight tests are compared to truth data as estimated using a global positioning system sensor to demonstrate the effectiveness of the algorithm. Robustness properties of the algorithm are explored using simulation studies.

The paper studies instantaneous Doppler signature extraction from within a very high frequency (VHF) band spectrogram presented by the authors in previous work. The context of the current method is long-range aircraft detection by VHF Doppler effect. The method proposed calculates bistatic radar (BR) cross-section (BRCS) profiles and the correlation between them for different types of aircraft. The analysis is based on data represented by automatic dependent surveillance-broadcast (ADS-B) trajectory collection and passive BR with TV station as an illuminator of opportunity. Throughout the analysis, ADS-B data on location of an aircraft were adjusted with the use of extracted Doppler shift information. Then, this ground truth information on location was used for proper evaluation of BRCS profiles and, finally, for validation of the extraction method. The method is able to classify common intercontinental aircraft by size class with 70% accuracy from a 100-km distance by using an illuminator of opportunity located 300 km away.

Nowadays most of modern digital TV and broadcast systems adopt a novel network technology, namely single frequency network (SFN). In SFN the same data stream is broadcasted from multiple transmitters in the same frequency band. Thus SFN-based passive radar (SPR) could generate multiple simultaneous measurements from the same target, introducing a measurement-to-transmitter association ambiguity (MTAA). False association could lead to ghosts. In this paper, we present a solvability analysis for MTAA under 2-dimensional (2-D) space and well-separated targets case. The purpose of this solvability analysis is to obtain the simplest sufficient condition for resolving MTAA. Besides, an association hypothesis decision (AHD) method is proposed for deghosting. Numerical simulations demonstrate that AHD is an effective method against ghosts under the SPR scenario.

A novel jamming method based on circularly moving strong scatterers is proposed. The jamming signal model is presented first, and the corresponding imaging results are derived through a range-Doppler algorithm. Detailed analysis shows that the proposed method can decrease the correlation, produce interferometric phase bias, result in failure of phase unwrapping, and reduce the accuracy of the digital elevation model. Simulation results are provided to verify the effectiveness of the proposed method.

This paper proposes a new radar/sonar processing architecture that enables a low-rate time-sampling rate while still producing a high-resolution range profile over a narrow range-window. This new architecture generalizes the conventional “stretch processing” architecture (which employs linear frequency modulated (LFM) waveforms that suffer from high range-sidelobes), to nonlinear frequency modulated (NLFM) waveforms, which can lower the range-sidelobes without tapering. Computational results demonstrate both the estimation efficacy and the time-sampling efficiency of the proposed scheme.

A conceptual radar platform design for detecting space debris down to 1 cm in diameter is presented for space situational awareness. Using known imaging algorithms, we show it is possible to overcome the problems associated with the typically small radar cross sections to yield a radar-attributable vector. Given the latter, the problem of matching the observation to a unique piece of debris is tackled independently of the imaging, or the detection, problem.

Frontal radar imaging of human activities may be useful in certain applications, such as through-wall surveillance, where cameras and x-ray sensors cannot be deployed. High-resolution radar images are currently obtained using electrically large antenna apertures operating at high frequencies. However, high frequencies are heavily attenuated by most walls. Also, the implementation of a radar with lots of array elements and associated data acquisition channels is costly and complex. In this paper, we propose methods to generate high-resolution Doppler-enhanced radar images of moving humans at low carrier frequencies with limited number of antenna elements. When a human moves, different body parts give rise to distinct Doppler returns. The key feature of our method is to dynamically resolve multiple body parts of the human across three dimensions: Doppler, azimuth, and elevation. The additional Doppler dimension allows us to relax the resolution requirements in terms of the carrier frequency and number of array elements across the other dimensions. We further reduce the number of array elements below the Nyquist limits by incorporating compressed sensing principles into two-dimensional beamforming because compressed sensing is particularly suited for solving certain types of under-resolved problems.We test our technique on simulated electromagnetic radar scattered data from a moving human for different radar configurations.We also study the robustness of the proposed technique to noise.

Synthetic aperture radar (SAR) gives not only the opportunity to image targets with high resolution but also to measure their position and velocity. Without acceleration (constant speed), the position and velocity both in range and azimuth can be estimated using a multichannel SAR system. This paper introduces a method to find the lower bound on variance of estimate of position and velocity for targets. The method is based on the assumptions needed for the Cram´er-Rao lower bound (CRLB). The method works for both narrowband (NB) and ultrawideband (UWB) SAR systems. For demonstration, a monostatic single-channel UWB SAR system operating at low frequency is used. Thus, only lower bounds for estimate variance of azimuth, range, and relative speed are derived.