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Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity. [ABSTRACT FROM AUTHOR]

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

The signal coherence on target is vulnerable toconfiguration perturbationsof an airborne distributed radar system where radar nominal positions are disturbed by unknown position errors to radar actual positions in three-dimensional space. This article copes with the configuration calibration problem of an airborne distributed radar system using several auxiliary devices in known locations, e.g., ground auxiliary receivers. To warrant parameter identifiability, for given transmitted signals from radars, two types of echoes are exploited, including the internal ones received by radars and external ones received by auxiliary receivers. The range measurements can be extracted from these echoes subsequently. The theoretical analysis claims that an identifiability condition for configuration calibration, precluding pathological configurations, is to have at least two auxiliary receivers when the location of one radar is known. Besides, configuration calibration is a highly nonlinear problem due to the coupling of position errors between two different radars and to the existence of second-order position error terms in the range measurement equations. We introduce the novel idea of auxiliary ranges to eliminate the second-order position error terms and the iterative fashion to handle the coupling in the methodology to enable the formulation of pseudolinear equations, thereby leading to the establishment of an algebraic solution for radar position estimation. Numerical simulations are included to corroborate the theoretical results and to demonstrate the superiority of the devised method in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A spectrum sharing radar can be guided by a cognitive decision process to determine the optimal radar operating frequency as the spectral environment changes. This decision process utilizes spectrum sensing or spectral prediction to determine the optimal radar transmission for a given situation. The radar transmitter power amplifier performance varies with frequency and bandwidth of the applied waveform, thus adaptive impedance tuners are useful in maximizing the transmitted power and radar range as the transmission frequency range is varied. Since high power handling is required in radar transmissions, and mechanically actuated impedance tuners presently demonstrate the best power handling, the time required to tune is often orders of magnitude greater than the pulse repetition interval. As such, the relatively lengthy impedance tuning operations should be guided to maximize the average output power as the system transitions between different center frequencies, bandwidths, and waveforms over time. This article presents an algorithm that performs impedance tuning with an evanescent-mode cavity tuner based on an average performance gradient computed for multiple transmit pulses. Comparison of test results with traditionally measured amplifier load-pull data shows that the transmitter is effectively optimized for maximum average output power. [ABSTRACT FROM AUTHOR]

In this article, semidefinite programming (SDP) solutions are proposed for the elliptic localization problem in asynchronous radar networks, where the transmitters are subject to clock offsets. Relaxing the nonconvex problem into different convex forms, we design tight SDP (TSDP) and global SDP (GSDP) solutions for this problem. The TSDP solution includes several SDP cones to solve the problem, and the correlation is not considered. Hence, the TSDP solution performs poorly in the presence of correlated noise. We further put forward the GSDP solution to improve the performance by introducing a new GSDP form, which includes only one SDP cone to handle the correlated noise. We also theoretically prove that the GSDP problem is tight enough so that its performance is able to sufficiently approach the Cramér–Rao lower bound (CRLB) accuracy. The simulated results show that the TSDP solution can provide the comparable performance with the GSDP in the absence of related noise. The performance of the GSDP solution can almost attain the CRLB accuracy using less receivers. [ABSTRACT FROM AUTHOR]

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target's motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm's robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking. [ABSTRACT FROM AUTHOR]

Asan important means in low-altitude airspace surveillance, passive radar has been widely studied. With the increasing complexity of the low-altitude environment, the urgent need for radar's capability of automatic target recognition (ATR) emerges. In the field of ATR for passive radar, the method based on radar cross section (RCS) is an important direction for the simplicity of feature extraction. In this article, we exploit the compensated quasi-echo-power (CQEP), i.e., quasi-RCS, to realize ATR in passive radar. We propose an ATR method on the basis of the angular diversity. First, we divide the angular space, which consists of four components of the incident angle and scattering angle of electromagnetic wave, into multiple subspaces. Then, we build a subrecognizer in each angular subspace according to the CQEP distribution characteristics. Each receiving station of the multistatic passive radar recognizes targets in an angular subspace corresponding to its observation and outputs a preliminary decision. Based on the preliminary decisions of all receiving stations, a voting strategy is adopted to make the final decision on the unknown target class in the fusion center. Experimental results based on real-life target data show that the proposed method has a significant improvement on the average correct recognition rate with fusion of three receiving stations, which validates the proposed method. [ABSTRACT FROM AUTHOR]

We derive a detector function for quantum two-mode squeezing (QTMS) radar and noise radar that is based on the use of a generalized likelihood ratio (GLR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the GLR detector, we derive a detector function which approximates the GLR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the GLR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is that there exists a parameter regime in which a previously-studied detector outperforms the GLR detector, contrary to the intuition that LR-based tests are optimal or nearly so. This is because neither the Neyman–Pearson lemma, nor the Karlin–Rubin theorem which generalizes the lemma to composite hypotheses, hold in this particular problem. However, the GLR detector remains a good choice for target detection in certain regimes. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods. [ABSTRACT FROM AUTHOR]

The cylindrical polarimetric phased array radar (CPPAR) project acts as a case study in cylindrical array design for applications in dual-polarized weather radar, allowing fundamental research such as beamformer optimization and weather measurement verification to be conducted. Developed at the University of Oklahoma’s Advanced Radar Research Center (ARRC), the CPPAR system relies on GPS-disciplined reference clocks for its transceivers and nearly synchronous triggering to transmit and receive bistatically from an isolated measurement horn located in the main array’s far-field, allowing the array’s element and beam patterns to be measured. This measurement scheme introduces phase distortion resulting from time-varying differences between the two reference clocks’ frequencies, a phenomenon which adds a time-varying bias to baseband phase measurements, rendering them unusable without significant post-processing. In this article, our approach to managing this and other measurement-specific challenges is detailed. To provide appropriate background information, the CPPAR’s relevant high-level system design, control systems, and theory is also discussed. Lastly, our method for deriving array bias terms from measured element patterns is detailed. The procedures outlined in this article were successfully deployed to measure element patterns and in turn derive power and phase calibration weights for the system, which ultimately allowed for the completion of two major experiments using the platform. [ABSTRACT FROM AUTHOR]

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar’s waveform catalog. [ABSTRACT FROM AUTHOR]

Range-dependent clutter suppression is a challenging problem in nonsidelooking, bistatic, and conformal antenna array airborne radar, especially in the presence of range ambiguity. Superimposed stepped frequency (SSF) radar employs a small frequency increment across the stepped signals in each pulse, which introduces a dimension related to slant range. In this article, an airborne radar framework is established, which takes SSF signal as the transmit waveform. Thus, a two-stage adaptive clutter suppression method is proposed, which utilizes the degrees of freedom in range, space, and time domains provided by airborne SSF radar. In the first stage, the secondary range dependence compensation approach is adopted to distinguish the clutter of each range region in the carrier frequency domain. Then, a target-free covariance matrix is estimated by the compensated data, and the covariance matrix is used for the range-ambiguous clutter separation in the carrier frequency domain. Thus, the space-time snapshot of each range region can be extracted. In the second stage, a clutter segmentation processing method is devised for residual clutter suppression. For the near-range unambiguous clutter, the traditional clutter compensation approach is applied to further align the spectrum distribution of clutter, while the conventional space-time adaptive processing is utilized to suppress far-range clutter directly. Since the proposed method takes full advantage of the range dimension and the two stages of adaptive processing are data-dependent, excellent clutter suppression performance can be obtained. The proposed method can also extract the real range of the target since each range region is processed separately. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, we develop a new waveform codesign approach for radar–communications spectral coexistence using a decision-theoretic framework called partially observable Markov decision process (POMDP). The POMDP framework’s natural look-ahead feature allows us to trade off short-term for long-term performance, which is necessary in waveform codesign problems with competing objectives and dynamic user needs. As POMDPs are computationally intractable, we extend two approximation methods called nominal belief-state optimization and random-sampling multipath hypothesis propagation to make the codesign approaches tractable. [ABSTRACT FROM AUTHOR]

When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space–time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space–time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is first carried out in this article, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with iterative adaptive approach is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter nonstationary compensation along spatial and temporal directions. Finally, a time-domain sliding window-based subspace projection method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Frequency diverse array (FDA), which employs a small frequency increment across its array elements to generate controllable degrees-of-freedom (DOFs) in range dimension, has attracted extensive attentions in recent years. In order to extract and exploit the DOFs in range and angle dimensions, FDA has been combined with multiple-input multiple-output (MIMO) technique, which shows sufficient performance improvement in clutter suppression in scenarios with range ambiguities. Therefore, clutter rank evaluation is an important issue for FDA-MIMO radar mounted on a moving platform. In this article, a clutter rank evaluation criterion is developed with respect to different transmit/receive array configurations and different number of range ambiguities. It is based on a subspace transformation matrix that decouples the clutter subspace and allows a direct determination of clutter rank. With the proposed clutter rank evaluation criterion, the maximum resolvable number of range ambiguities can be determined, which is bounded by the system's DOFs. Three cases according to different relationships between uniform array element spacings are studied. Then, the aforementioned clutter rank evaluation method is extended to nonuniform array configurations. Moreover, the robustness of the method is studied in the presence of gain-phase errors. Several simulation results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article deals with the multiple-input–multiple-output (MIMO) radar beampattern design in an effort to the coexistence with multiple communication systems. A waveform optimization model accounting for the minimization of the beampattern integrated sidelobe level (ISL) along with the mainlobe width, peak-to-average power ratio, and energy constraints, as well as multispectral requirements where the interference energy injected by the MIMO radar in each shared frequency band in a particular direction, is precisely controlled to ensure the desired quality of service at each communication system. Through an equivalent reformulation of the original nonconvex problem, a polynomial-time sequential convex approximation (SCA) procedure that involves the tackling of a series of constrained convex problems is proposed to monotonically decrease the ISL with the convergence guaranteed to a Karush–Kuhn–Tucker point. Herein, to speed up the convergence, a fast iterative algorithm based on the alternating-direction-method-of-multipliers framework is introduced to globally solve the convex problems during each iteration of the SCA procedure. Numerical results are provided to assess the proposed algorithm in terms of the computational complexity, the achieved beampattern, and spectral compatibility with some competitive counterparts available in the open literatures. [ABSTRACT FROM AUTHOR]

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch's specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code. [ABSTRACT FROM AUTHOR]

This article addresses the problem of tracking weak and extended targets in the clutter for marine radar systems. In the proposal, multiple kernelized correlation filters (MKCFs) incorporate a low-threshold constant false alarm rate and segmentation model under the principle of multiframe track before detect (MF-TBD). By setting the similarity score of the correlation filter as the test statistic, the maximization of the integration over frames can be solved by the essentially exhaustive searching of the MKCF. Since the correlation filter measures the similarity of the intensity distributions in the extended templates, the proposed method can differentiate the weak target from the surrounding clutter and other targets at a close range. Compared to other amplitude-based MF-TBD methods, it performs better in detecting and tracking extended targets in the real-radar data with heavy clutter caused by floating sea ice and the simulations with Rayleigh and $K$-distributed sea clutters. [ABSTRACT FROM AUTHOR]

Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results. [ABSTRACT FROM AUTHOR]

Electronic countermeasures (ECMs) against a radar are actions taken by an adversarial jammer to mitigate the effective utilization of the electromagnetic spectrum by the radar. On the other hand, electronic counter-countermeasures (ECCMs) are actions taken by the radar to mitigate the impact of ECMs so that the radar can continue to operate effectively. The main idea of this article is to show that ECCMs involving a radar and a jammer can be formulated as a principal–agent problem (PAP)—a problem widely studied in microeconomics. With the radar as the principal and the jammer as the agent, we design a PAP to optimize the radar's ECCM strategy in the presence of a jammer. The radar seeks to optimally trade off the signal-to-noise ratio of the target measurement with the measurement cost: cost for generating radiation power for the pulse to probe the target. We show that for a suitable choice of utility functions, the PAP is a convex optimization problem. Furthermore, we analyze the structure of the PAP and provide sufficient conditions under which the optimal solution is an increasing function of the jamming power observed by the radar; this enables computation of the radar's optimal ECCM within the class of increasing affine functions at a low computation cost. Finally, we illustrate the PAP formulation of the radar's ECCM problem via numerical simulations. We also use simulations to study a radar's ECCM problem, wherein the radar and the jammer have mismatched information. [ABSTRACT FROM AUTHOR]

In an airborne maritime radar, inverse synthetic aperture radar (ISAR) is used to image and classify non-cooperative targets. In this article, we investigate a 3D-ISAR as an alternative representation that can potentially improve the final target classification by automatically extracting key features from the radar data, such as the length, width, and height of a target. The technique works by estimating the target scatterers’ positions using measurements from multiple ISAR images. This has previously been achieved using a single receive channel that exploits the temporal motion of the target or with a multichannel dual-baseline interferometric system that uses the phase differences in both the along- and across-track directions. In this article, we combine aspects of these two techniques using a single along-track baseline suitable for airborne maritime radar systems. [ABSTRACT FROM AUTHOR]

Helicopter main rotor blade flashes in radar measurements are an often-used feature for target classification. While focus is often placed on being able to detect them, little attention has been given to what is required to ensure that they are reliably and persistently intercepted. Using mathematical analysis and simulation, this article explores the probability of helicopter blade flash interception in search radar. The effects of radar antenna azimuth beamwidth, scan rate, and waveform on the average probability of intercept for two-bladed helicopter blade flashes (a worst-case scenario) are examined. The results presented provide an upper bound on achievable flash detection performance and can be used to understand and optimize the performance of helicopter classification modes. [ABSTRACT FROM AUTHOR]

A dual-polarized radar system is utilized for non-contact sensing applications involving rotational motion. Polarization-centric methods are employed to sense and characterize responses from an electric motor with various bearing faults and to sense responses from a high-velocity fan. Conventional characterizations, such as power spectral densities and micro-Doppler responses, are compared with polarization-based statistical approaches such as polarization entropy and a newly proposed micro-Entropy approach inspired by the micro-Doppler approach. [ABSTRACT FROM AUTHOR]

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using a linear-frequency-modulated continuous-wave automotive radar. To mitigate the problems, we introduce a simple Doppler–range processing (DRP) algorithm by first performing Doppler processing via fast Fourier transform (FFT) across slow-time samples, followed by a simple interpolation step, and then range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve full range and full velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2-D-FFT-based range–Doppler processing approach, computationally much more efficient than existing approaches. The proposed DRP algorithm can automatically resolve the velocity ambiguity problems. We analyze its velocity ambiguity mitigation capability in relation to the radar bandwidth and the number of slow-time samples within a coherent processing interval. The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Unlike the conventional phased array, the frequency-diverse array (FDA) employs a tiny frequency increment across the array elements, which is capable of providing range–angle–time-dependent beampattern, and thereby offers potential benefits in target localization and interference mitigation. Reported literature on FDA mostly focuses on its intrapulse (fast-time) property and ignores the interpulse (slow-time) feature induced by frequency offset. In this article, incorporating the neglected inherent slow-time coding feature, a novel slow-time FDA multiple-input multiple-output (FDA-MIMO) technique is proposed for the space-time adaptive processing (STAP) radar. The new coding scheme can separate transmitting (Tx) signals via slow-time Doppler filtering, and the problem of signal aliasing is tackled by appropriately designing the slow-time codes. At the receiving (Rx) side, two Rx schemes are devised for recovering range-dependent Tx degrees-of-freedom. Moreover, the relevant clutter rank and the rule for code design are derived in detail. The outstanding merits of the proposed slow-time FDA-MIMO STAP radar consist of the following: There is no specific restriction on the probing waveform; the negative effect of time-variant pattern is perfectly eliminated; and both the target localization accuracy and clutter suppression performance are superior over the state-of-the-art FDA radar systems. Numerical results corroborate the superiorities of the proposed waveform strategy for STAP application. [ABSTRACT FROM AUTHOR]

The problem of the constant false alarm rate (CFAR) detection of bistatic space based radar (B-SBR) is addressed under the background of strong and nonhomogeneous clutter. We propose a novel and simple cell averaging (CA)-CFAR-like (CA-CFAR-L) detection method based on the Gerschgorin circle theorem for B-SBR. First, we investigate the potential of a novel test statistic TCA-CFAR-L exhibiting better properties about discrimination and CFAR using the Gerschgorin circle theorem, and the test statistic TCA-CFAR-L is constructed as the ratio of the sum of the Gerschgorin radii of the covariance matrix of the cell under test to the sum of the Gerschgorin radii of the mean covariance matrix of the reference cells. Second, we investigate the CA-CFAR-L detector for B-SBR using the constructed test statistic TCA-CFAR-L. Under the background of the K-distribution clutter model and the clutter model for B-SBR, respectively, we verify the performance of the proposed CA-CFAR-L detector, the LE mean matrix CFAR detector and the Fast Fourier Transform (FFT)–CFAR detector. Also, we analyze the experimental data for the IPIX radar. The simulation results and experimental data analysis demonstrate that the detection performance of the detector is better than the LE mean matrix CFAR detector and the FFT–CFAR detector, and the proposed method has less computational complexity. These will provide an important theoretical basis and new ideas for radar target detection in the future. [ABSTRACT FROM AUTHOR]

We consider a dual-function radar communication (DFRC) system employing frequency hopping (FH) radar waveforms. Each communication symbol is represented by a phase-modulated pulse sequence that multiplies the radar hops in fast-time and transmitted from a multiple-input multiple-output radar platform. In this respect, the DFRC system considered follows the code-shift keying (CSK) signal embedding strategy. The phase modulations include phase-shift keying (PSK), derivative PSK, and continuous phase modulation. The CSK pulse sequences and the FH code are jointly designed to reduce range sidelobe levels (RSLs) and combat range sidelobes modulation. It is shown that the joint optimization using the genetic algorithm yields desired CSK sequences and FH code matrix with less computational complexity compared to the exhaustive search algorithm. The power spectral density and detection performance of various DFRC systems are analyzed, underscoring the tradeoff between RSLs and spectral sidelobe levels. The proposed DFRC system with optimum phase sequences and hopping code achieves high data communication rates by permitting frequencies to be reused in the radar hops. [ABSTRACT FROM AUTHOR]

In this article, an active multireceiver low-THz imaging radar is used to generate 3-D images of automotive environments by combining time (difference) of arrival (TDoA) information with backprojection techniques to give height profiles of the objects in the scene. The large bandwidth of this system facilitates noncoherent height reconstruction using time of arrival alone. In doing so, the constraints on receiver coherency are relaxed compared to many height-finding techniques. 3-D reconstructions of common roadside targets and scenes are generated through a modified form of backprojection, adapted to use the time of arrival information of the signals from multiple receivers with different sensitivities, which are arranged in a quasi-multistatic configuration. Here, it is experimentally shown that the use of three vertically-aligned receivers is sufficient to provide a fine resolution in range, cross-range, and height. The system is used to generate 3-D reconstructions of a number of different scenarios, including pedestrians moving through a scene, and complex real-world road scenes. [ABSTRACT FROM AUTHOR]

In this article, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this article. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector, the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the GLRT detector for compound-Gaussian clutter with inverse Gaussian texture. Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure named $\alpha$ matched filter ($\alpha$-MF) detector and its adaptive version are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the $\alpha$-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in compound-Gaussian clutter with inverse Gaussian texture, which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio property relative to the estimate of the speckle covariance matrix and the Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data. [ABSTRACT FROM AUTHOR]

Owing to the continuous random and nonperiodic characteristics of the passive signal, the random range sidelobe coupling (RRSC) effects of the strong clutter (i.e., the direct path signal and the strong multipath signal) will exacerbate the performance of space-time adaptive processing (STAP) algorithm in airborne passive radar (APR). This article analyzes the influence of RRSC caused by strong clutter on detection range cell of interest and illustrates that strong clutter should be suppressed before applying the STAP algorithm. A cascaded least square (CLS) algorithm based on the directional dependence of clutter Doppler frequency is proposed for side-looking APR, where the first least square (LS) algorithm is performed to achieve the amplitude estimation of the Doppler-shift echo signal, and the channel amplitude and phase error correction factor can be obtained by the estimated amplitude information of the direct patch signal and the APR geometric configuration. The second LS is employed to further estimate the amplitude information of the strong clutter, and the resulting weight vector can be utilized for clutter cancelation. To make the CLS algorithm more practical, a batch version of the CLS (CLS-B) algorithm is proposed in this article. The advantage of the method is that it cannot only cancel clutter along the clutter ridge but also effectively prevent the suppression of targets falling in the clutter Doppler band. In addition, the proposed CLS algorithm performs well in the presence of the channel amplitude and phase error. A series of simulations are conducted to test the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article demonstrates the use of a neural network to replace the nonlinear optimization at the core of a fully adaptive radar (FAR) system. Use of the neural network is shown to reduce computational complexity, leading to faster updates of the radar parameters. The feed-forward neural networks used were trained using the Levenberg–Marquardt algorithm along with a generalized regression neural network architecture. Training data was obtained from an existing FAR optimization system using the system’s parameter selections and target states for single-target tracking in range and velocity. The trained networks were then used to perform radar parameter selection based on the predicted (forward propagation of prior probabilities) probabilities represented by the target state (target range, velocity, and signal-to-noise ratio) and error variance (target range and velocity variance). The trained neural network was able to reduce the average optimization time by an order of magnitude while obtaining on average a lower resource consumption and similar radar task performance. [ABSTRACT FROM AUTHOR]

Bistatic radar signal processing often assumes a constant velocity for target detection. The bistatic geometry alters, however, the effectively measurable velocity in relation to the target position and motion even if the target has a constant ground speed. Hence, the bistatic geometry induces instantaneous bistatic target acceleration. This is especially the case if a straight-line target trajectory cuts across several closely spaced velocity isolines. The letter tries to quantify this geometry-induced instantaneous bistatic acceleration, in general, for straight-line target trajectories. Thereby, position-dependent acceleration values are derived for a practical use case. [ABSTRACT FROM AUTHOR]

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model. [ABSTRACT FROM AUTHOR]

A new algorithm for passive bistatic radar (PBR) localization is proposed via joint exploitation of multiple illuminators of opportunity and measurements gathered by a colocated active radar. Ad-hoc constraints within the localization process are bestowed accounting for both a priori information on the PBR receive antenna main-beam size and the uncertainty characterizing active radar data, i.e., the root mean square error (RMSE) of range/bearing measurements. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained least squares framework. The resulting non-convex optimization problem is globally solved providing a closed-form estimate in Cartesian coordinates. The performance of the proposed estimator, in terms of RMSE, exhibits sensible improvement with respect to counterparts. The proposed technique is also applied to the case of a dynamic scenario for a bi-sensor surveillance system, where the active rotating platform acquires measurements at each scanning period. The results show the localization performance improvement achieved when the system is complemented with a PBR staring in a specific search sector of interest. [ABSTRACT FROM AUTHOR]

Electronic attack devices can interrupt a radar system by emitting jamming signals. This function typically depends on the support from electronic support measure (ESM) devices, which can capture and identify radar signals in the air. A radar system can avoid being interrupted by confusing adversarial ESM devices. In this article, at the background of colocated multiple-input multiple-output radar, we present an active antijamming method that aims at confusing adversarial ESM devices by masking radar signals with accompanying signals. Masking signals can be transmitted in either the pulsed mode or the periodic continuous-waveform mode. They can be either constant-modulus or with amplitude modulation. The masking signal will cover the radar signal in both time and frequency domains, in order to make adversarial ESM devices find it hard to determine whether an intercepted signal source is a spoofing device or an operating radar. We suppress the impact of masking signals on radar-matched filters, measured by the integrated sidelobe level and peak sidelobe level of their cross-correlation sidelobes, through optimization of masking signals. Masking signals whose chip duration is shorter than that of radar signals are considered for a lower cross-correlation sidelobe level. It is termed as the sequence spread spectrum method, and this method is found critical to obtain good cross-correlation sidelobes, as indicated in numerical results with several kinds of well-designed radar signals. In concerned simulation scenarios, a good masking signal tends to focus its power on frequency bands, where the radar signal power is low. [ABSTRACT FROM AUTHOR]

This article studies the problem of radar detection of correlated gamma-fluctuating targets in the presence of clutter described by compound models with correlated speckle. It models signals from a pulsed radar with pulse-to-pulse correlation of components of the range-gated returns from a target with added thermal noise and compound Gaussian clutter. If the correlation is not accounted for in a radar model, the required signal-to-interference ratio as predicted by the model for a given probability of detection will be incorrect, resulting in overestimated performance. Although more generally applicable, the focus of this article is on airborne maritime radar systems. Hence, $K$ -distributed sea clutter is used as the main example. Detection via square-law noncoherent pulse integration is formulated in a way that accommodates arbitrary partial correlation for both target radar cross section (RCS) and clutter speckle. The obstacle to including this degree of generality in previous work was the fact that Swerling’s original characterization of the standard RCS fluctuation classes based on specifying distributions for the power is not sufficient to accommodate inclusion of both sources of correlated returns (i.e., target and clutter speckle) in the case of gamma-fluctuating targets. An extension of the model is required at the quadrature component (i.e., voltage) level, as phase relationships can no longer be neglected. This is addressed in the present article, which not only postulates an extended model, but also demonstrates how to efficiently compute it, with and without a number of simplifying approximation schemes within the framework of the saddle-point technique. [ABSTRACT FROM AUTHOR]

Because the maximum radiation direction of the end-fire array antenna points to the axis of the array and the small element spacing required to suppress the grating lobes, the mutual coupling effect between the arrays is more serious than that of the broadside array. The article first established the end-fire array antenna pattern model and clutter signal model with the effect of mutual coupling. The clutter signal model includes two cases, ideal mutual coupling effect and mutual coupling error. On this basis, we take a ten-element uniform linear array using CST Microwave Studio to validate our model. Then we analyze the clutter distribution characteristics of the end-fire array airborne radar and the effect of mismatch on the spatial steering vector. Finally, an adaptive mutual coupling compensation method based on covariance matrix reconstruction is proposed. In general, clutter suppression methods with end-fire array take no account of the mutual coupling error changing over time, but the proposed method first adaptively estimates the mutual coupling error based on the airborne radar echo data and uses the estimated normalized impedance matrix to compensate the spatial steering vector, then combines the system parameters to construct the clutter covariance matrix. The simulations indicate that this method accurately estimate the impedance matrix, and it eliminates the clutter power reduction and spatial steering vector mismatch caused by mutual coupling error. Moreover, it also solves the serious non-stationary clutter problem of the end-fire array airborne radar. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases. [ABSTRACT FROM AUTHOR]

We propose a technique for landmine detection using forward-looking ground-penetrating radar. The detector is applied to radar images obtained from multiple viewpoints of the region of interest and is based on a robust version of the likelihood ratio test (LRT). We incorporate the statistical dependence between multiview images into the test via copula-based model. The test is designed to maximize the worst-case performance over all feasible pairs of target and clutter distributions, thereby eliminating the need for strong assumptions about the image statistics. We evaluate the detection performance of the proposed technique for different copula functions representing the dependence structure. Using electromagnetic modeled data of shallow-buried targets under varying ground surface roughness profiles, we demonstrate the superiority of the robust copula-based detector over existing parametric and robust LRT approaches designed under the assumption of statistical independence of multiview images. [ABSTRACT FROM AUTHOR]

Sample clock offset must be estimated in order to correctly demodulate an OFDM signal. Estimates are also required in passive radar processing, to produce a reference signal, which is properly matched to the transmission. A recently proposed clock offset estimation method for passive radar is found to be overly sensitive to measurement noise. However, the radar ambiguity surface is found to be unaffected by the specific errors engendered by this noise. The reasons for this are explored. An updated method based on state-space modeling and Kalman smoothing is proposed. This is found to restore clock offset estimation performance in cases of higher noise, achieving this with low processing latency. These improvements enable the method to be employed for other applications requiring high quality, time-critical clock offset estimation. [ABSTRACT FROM AUTHOR]

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions. [ABSTRACT FROM AUTHOR]

This article aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with nonuniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signal-dependent clutter is first established. Then the 2-D range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant nonconvex problem, the sequential greedy optimization algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent framework and a Lagrangian-based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter. [ABSTRACT FROM AUTHOR]

Receiver nonlinearities pose a serious risk to the functionality of modern radars as they can compromise the sensor's immunity to interfering signals. With the radio frequency (RF) spectrum becoming increasingly crowded, it is now more important than ever that the sensor can maintain system performance when exposed to mutual interference. In this article, we present a nonlinear compressive sensing (NCS) solution, which, unlike the standard nonlinear equalization (NLEQ) techniques, is designed around the forward nonlinearity rather than the inverse. Importantly, in this article, the NCS theory is extended to include nonlinear memory. Furthermore, a radar-specific formalization is derived, which allows the nonlinear optimization to exploit the unique properties of pulsed-Doppler radar processing. As a result, the NCS solution can successfully restore system sensitivity back to the linear case when in-band interference drives the radar receiver into its nonlinear regime. Additionally, it is shown that the technique can consistently mitigate complex nonlinear memory effects generated in the RF receiver. This is a significant result as it proves that forward modeling techniques are a viable alternative to NLEQ. This is of particular importance to radar systems as they provide a far more explicit formalization to mitigate nonlinear memory effects. [ABSTRACT FROM AUTHOR]

Doppler ambiguity in the range-Doppler domain of linear frequency modulated (LFM) continuous wave radars occurs when a target’s Doppler frequency exceeds half of the chirp repetition frequency. Doppler ambiguity is common in a variety of civil and military radar applications, and an extension of the maximal unambiguous Doppler shift is critical for their practical use. This article utilizes previously neglected high-order phase terms in the received LFM radar echo for extension of maximum unambiguous velocity. A computationally feasible velocity estimation algorithm using hypotheses testing with local maximum likelihood is derived. The ability of the proposed low-complexity algorithm to estimate previously ambiguous target velocity is investigated using recorded automotive radar measurements and via simulations. The performance of the proposed algorithm is evaluated using a novel model of the Doppler estimation errors that accounts for Doppler ambiguity. The ability of the derived model to predict the threshold phenomenon is demonstrated via simulations. [ABSTRACT FROM AUTHOR]

The detection and tracking performance of passive radar systems can be enhanced by exploiting multiple emitters and one receiver site by allowing for multiple bistatic geometries. This article investigates the measurement accuracy of passive radar systems that employ the advanced television system committee (ATSC 1.0) signal as a signal of opportunity. The monostatic and bistatic modified Cramér–Rao lower bound for the range and velocity measurements are derived as a quantitative way to evaluate the performance variation when exploiting multiple transmitters. The effect of 3D target position on the signal-to-noise ratio, with respect to stationary transmitters and receiver, is highlighted. Furthermore, signal-to-interference-and-noise ratio modeling is included by considering the direct signal interference suppression performance for a set of transmitters. An example in Columbus, Ohio, for a single receiver and multiple digital television transmitters that are spatially distributed is presented to find the optimal emitter that yields the best range and velocity measurement in the analyzed area. Target-tracking experiments show an agreement between the optimal emitter map and the tracking quality for the set of transmitters. [ABSTRACT FROM AUTHOR]

Polygonalized phased arrays are a compromise between planar arrays and cylindrical arrays that provide surveillance radar systems improved surveillance time from a convenient form factor while also enabling many key aspects of ubiquitous radar. Unlike prior investigations of horizon surveillance, this article investigates the benefits offered by cylindrical and polygonalized array geometries, where elements pointing in differing directions can be combined coherently. [ABSTRACT FROM AUTHOR]

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this article. We propose two novel learning-based approaches to waveform and detector design based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and the detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models, it is shown that radar learning can be robustified to uncertainties about environment by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints. [ABSTRACT FROM AUTHOR]

For over-the-horizon radar (OTHR)-based target tracking, the reflecting height of the ionosphere which reflects radar signals is important. Existing methods assume that this height is exactly known a priori. In practice, however, we can only determine its range, not its value. To circumvent this problem, we propose to use a multiple-model approach in which each model corresponds to a specific height for OTHR tracking. Further, existing methods do not provide a systematic and effective solution to track confirmation and termination, and mean-square-error (MSE) matrices of state update calculated by these methods are in error. To overcome these drawbacks and estimate the target state, an autonomous multiple-model target perceivability (TP)-based multipath probabilistic data association (TP-MPDA) tracker is proposed, where TP probabilities are derived for track confirmation, termination, and refinement, and MSE matrices of state update are corrected. Seeing that a small fixed set of models does not cover a large height range well, an expected-mode augmentation TP-MPDA tracker is proposed, which augments a basic model set by an online adaptive expected height. Since the model is nonlinear in the height, we also present a best model augmentation TP-MPDA tracker, where a basic model set is augmented by the best model that minimizes a Kullback–Leiber divergence. Simulation results demonstrate that the proposed algorithms are effective and have better performance than the existing MPDA algorithm. [ABSTRACT FROM AUTHOR]

Detection and tracking of small targets in sea clutter using high-resolution radar is a challenging problem. Recently, a Bernoulli track-before-detect (TBD) filter has been developed for an airborne scanning radar in the maritime domain, with the purpose of detection and tracking of short-exposure targets by using a fast scanning mode (i.e., short dwell-time and noncoherent integration). This article investigates the potential benefits of coherent radar processing and exploitation of Doppler information in TBD, when the radar operates in a slower scanning mode (i.e., using longer dwell times). For this purpose, a new Bernoulli TBD filter is developed, which is capable of processing full three-dimensional (range-azimuth-Doppler) radar data. The amplitude of sea clutter is modeled using the K distribution with Doppler-dependent shape and scale parameters. Numerical results indicate that the new Bernoulli TBD outperforms the fast scanning TBD filter, at signal-to-interference ratios below 6 dB. [ABSTRACT FROM AUTHOR]