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This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-Accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity. This work was supported in part by the National Natural Science Foundation of China under Grant 61771110, Grant U19B2017, and Grant 61871103, in part by the Fundamental Research Funds of Central Universities under Grant ZYGX2020ZB029, in part by Chang Jiang Scholars Program and the 111 Project under Grant B17008, and in part by the China Scholarship Council.

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 non-cooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This paper proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). Firstly, 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 non-adjacent 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 non-ideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multi-variate input conditions.

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., Comment: 16 pages, 9 figures. arXiv admin note: text overlap with arXiv:2010.15698

Range-dependent clutter suppression is a challenging problem in non-sidelooking (NSL), 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 induces a dimension related to slant range. In this paper, an airborne radar framework is established with 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 (SRDC) 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.

When range ambiguity and range dependence coexist in forward-looking ground moving target indication (GMTI) radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding (APC) technique is presented in this paper. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range-bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity are verified by the numerical simulation experiments.

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using linear frequency-modulated continuous wave (LFMCW) 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 velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2D-FFT based Range-Doppler Processing (RDP) 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 (CPI). The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples.

This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable., Comment: one column, 39 pages

Classifiers based on machine learning are usually trained to distinguish between several known classes. For an electronic intelligence application, however, it is of great importance to recognize if an intercepted signal belongs to an unknown radar emitter. In the machine learning literature, this task is called open-set recognition. This article investigates six approaches in several configurations to recognize unknown emitters. It is based on a hierarchical emission model that understands emissions as a language with an inherent hierarchical structure. We consider two general approaches, which are the ""memoryless"" Markov chain and the Long Short-Term Memory recurrent neural network, which is especially designed to ""remember"" the past. The performance is demonstrated with two evaluation metrics in ten scenarios that contain different combinations of known and unknown emitters. An evaluation with corrupted data provides an estimate on the methods' accuracies under challenging conditions. The results show that unknown emitters that do not use known waveforms are reliably recognized even with corrupted data, while unknown emitters that are more similar to known ones are harder to detect.

The plasma sheath will be generated on the surface of the reentry object due to the effect of aerodynamic thermoionization, causing multi-component signals with Doppler frequency components in the radar echo. This situation will further result in the appearance of false targets that seriously affect the detection of reentry targets by radar, causing positioning errors and even tracking losses. In this paper, the echo signal model of the plasma-sheath-covered reentry target is established, and the multi-domain characteristics of the echo signal in terms of time, time-frequency and range are analyzed, revealing the influence mechanism of plasma sheath on radar detection of reentry target. Through range-period analysis, the influence of intra-pulse Doppler frequency caused by plasma sheath is determined, as well as inter-pulse Doppler frequency on target range measurement. Theoretical analyses conducted in this study are verified by simulation results, laying theoretical foundation for effective detection and reliable tracking of plasma-sheath-covered reentry targets.

Target detection in high-frequency surface-wave radar (HFSWR) systems is a challenging task due to various sources of interference. In this paper, we propose a robust constant false alarm rate (CFAR) detector to solve the problem of target detection for HFSWR by exploiting the prior information on the detection environment. Clutter amplitudes in HFSWR are commonly assumed to follow the Weibull distribution. Additionally, distribution parameters of the clutter have nonlocal self-similarity, and targets have sparsity as well as a certain geometric structure. We design an objective function based on the Bayesian framework which consists of a data fidelity term and some regularization terms. Specifically, we use the nonlocal low-rank (NLR) regularization to represent the prior information regarding distribution parameters of clutter, and we adopt the combination of the sparsity and Laplacian regularization to represent the prior information regarding targets. The proposed objective function inherits advantages of the NLR, sparsity, and Laplacian regularization, thereby improving the performance of the proposed detector in nonhomogeneous clutter. By optimizing the proposed objective function, we obtain estimates of clutter statistics for every cell under test and perform the CFAR detection. Experiments on both simulated data and experimental HFSWR data demonstrate that the proposed detector has a good false alarm regulation property and detection performance in nonhomogeneous clutter.

This paper aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with non-uniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signaldependent clutter is first established. Then the two-dimensional 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 non-convex problem, the sequential greedy optimization (SGO) algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent (CD) 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.

In multitarget scenarios, the masking of small targets by large targets nearby may severely deteriorate radar detectability due to the range and Doppler sidelobes. In this article, a 2-D joint iterative adaptive filtering (2-D JIAF) method is proposed, by adopting the reiterative minimum mean square error (RMMSE) criterion to the outputs of a 2-D matched filter. The main advantages of the proposed method over the state-of-the-art (SOTA) methods, including modified adaptive multipulse compression and iterative adaptive approach, are twofold: 1) it is able to suppress the sidelobes in both range and Doppler dimensions and thus obtain an improved range-Doppler image; 2) the computational complexity is significantly reduced by adopting a small processing window in both range and Doppler dimensions. The derivation of 2-D JIAF is detailed and an efficient two-stage implementation is outlined. The performance of 2-D JIAF is validated with simulation results over a wide range of scenarios and compared with two SOTA approaches. The impacts of different parameters, including the number of iterations and the choice of the size of the processing window, are also extensively studied with Monte Carlo trials.

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.

This article concerns the issue of how to combine the multidomainradar information, including range–Doppler, time–Doppler, and time–range, for human activity recognition. Specifically, to fully make use of radar information, instead of using a single-domain spectrum as inputs, a novel hybrid neural network model is developed for exploring multidomain fusion of radar information. In doing this, three kinds of 2-D domain spectra are used in a fashion of supplementing each other with a hybrid framework that combines three models: 1-D convolution neural network, recurrent neural network, and 2-D convolution network. It is advantageous to use such a hybrid model to capture much rich features through multidomain feature fusion, so as to improve the accuracy of human activity recognition effectively. Experimental results validate the proposed method.

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.

A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramer–Rao bound.

The interrupted-sampling repeater jamming (ISRJ) is a kind of intrapulse coherent deception jamming, it produces false-target peaks, which obfuscate the real target detection and tracking. In this article, a novel recognition method is proposed to identify the false-target peaks caused by ISRJ. The proposed method is realized via the integration decomposition of pulse compression, and the intermediate data in pulse compression are extracted to identify the false-target peaks. Due to the time-sharing transmit–receive antenna of the jammer, the jamming signal is short or discontinuous pieces compared with the real echo. This intrinsic property provides important feature for jamming identification. In the proposed method, a variance value is generated from the intermediate data, to evaluate the temporal energy distribution evenness of the signal component corresponding to each target peak. Real target echo corresponds to small variance whereas the jamming signal corresponds to large variance. In this way, false-target peaks caused by jamming signal can be identified efficiently. Besides, the computational cost of the proposed method is low, thus, it is suitable for real-time applications in practical radar systems. Numerical experiments under different jamming parameters demonstrate the promising performance of the proposed false-target recognition method. Moreover, Monte Carlo simulations under different SNR levels verify the reliable classification capability of the proposed method. In summary, this article provides a new perspective for ISRJ identification, and it is also the proof-of-concept example for other potential applications concerning integration decomposition.

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.

In this article, we review and compare different methods for the localization of an illuminator of opportunity for passive radar systems employing cooperative targets. For assessment and comparison, we use numerical simulations and real-world radar measurements. To achieve reliable operation under real-world conditions, a correct and robust association of the bistatic radar target observations with ground truth information is necessary. We introduce a novel solution to this issue with an efficient preprocessing algorithm based on a planar approximation.

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.

In this article, we address the problem of robust adaptive beamforming in the presence of array sensor miscalibration. We consider the use of partly calibrated linear arrays, where only a small portion of sensors have been gain-phase aligned. Our solution is based on the interference-plus-noise covariance matrix (INCM) reconstruction principle. In our solution, the INCM is reconstructed by performing simultaneous interference localization and array calibration (SILAC). Toward this end, a novel virtual baseline extension technique is presented for high-accuracy SILAC. After SILAC, the interference and noise powers are estimated, and the INCM is reconstructed subsequently. No computations of integration/summation and nonlinear optimization are involved in our beamformer, which is termed as “INCM-SILAC” beamformer. Numerical examples are offered to validate the performance of the INCM-SILAC beamformer. A MATLAB code for reproducing the results of radar application example is available at https://github.com/jinhesjtu/SILAC.git

Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-of-arrival (DOA) estimation, a high number of receiving antenna elements is needed. The high dimension of the element level data inevitably leads to a large computational burden for the digital signal processing. This problem can be overcome by transforming the element level data into a lower dimensional beamspace. In this paper, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets pre-defined performance criteria by exploiting an acceptable trade-off between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. The design tool comprises a pre-calculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method.

Through-wall human sensing is of high interest for both military and nonmilitary applications, including counter-terrorism and law enforcement purposes, particularly in the modern, highly urbanized environment. Conventional active radar sensors possess good performance, however, at the expense of high cost and complexity of the transmitter/receiver design. This article demonstrates the effectiveness of a passive through-wall human sensing technique using opportunistic WiFi signals. With a simple software-defined radio receiver and advanced signal processing techniques, the position of indoor WiFi access point can be accurately localized, and its signal can be exploited to clearly detect the Doppler/micro-Doppler of human motions inside the room. Compared to the results in the current literature, this article demonstrates better detection sensitivity and capability to sense not only major motions such as walking and waving hand, but also very small human motions such as keyboard typing and breathing. In addition, an iterative adaptive approach is applied to effectively improve the Doppler resolution of WiFi passive radar. These enhanced sensitivities greatly expand the potential of WiFi passive radar for through-wall sensing applications.

The automatic identification system (AIS) is an essential and economical equipment for collision avoidance and maritime surveillance. However, AIS can be subject to intentional reporting of false information, or “spoofing”. This article assumes the vessel trajectory nominally follows a piecewise mean-reverting process; thereby, it addresses the problem of establishing whether a vessel is reporting adulterated position information through AIS messages in order to hide its current planned route and a possible deviation from the nominal route. Multiple hypothesis testing suggests a framework to enlist reliable information from monitoring systems (coastal radars and space-born satellite sensors) in support of detection of anomalies, spoofing, and stealth deviations. The proposed solution involves the derivation of anomaly detection rules based on the generalized likelihood ratio test and the model-order selection methodologies. The effectiveness of the proposed anomaly detection strategy is tested for different case studies within an operational scenario with simulated data.

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 Cramer–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.

The use of the polarization diversity of a target signal at a polarization-sensitive antenna array can enhance the target detection and tracking capabilities of a radar. In this article, the manifold separation steering vector modeling technique is used to develop a maximum likelihood method for joint direction of arrival (DOA) and polarization estimation. Manifold separation can incorporate antenna array nonideal characteristics (e.g., cross polarization, mutual coupling) into the estimation algorithm using array calibration measurements. In the proposed technique, the estimation problem is formulated as a generalized Rayleigh quotient minimization problem that is transformed into a determinant minimization problem. Both the azimuth and elevation angles are estimated using the fast Fourier transform. Unlike the existing manifold separation based polarimetric element space (PES) multiple signal classification method and the PES Capon method, the proposed method can obtain DOA and polarization estimates based on very small-size primary data samples, even with a single sample, which makes the proposed method more suitable for nonstationary target polarization. The performance of the proposed method is demonstrated through simulations. The Cramer–Rao lower bound for joint DOA and polarization is also used for comparison with empirical errors.

Recent publications have investigated and demonstrated new digital modulation techniques that allow realization of orthogonal frequency division multiplexing (OFDM) radar with relatively low hardware effort. This is especially interesting as the digitally defined signals offer additional freedom for multiplexing schemes of multiple-input multiple-output (MIMO) radar sensors. MIMO radar sensors rely on strictly orthogonal signals to distinguish between multiple transmitters (TX). To span a virtual array with a maximum number of antenna positions, a novel method that combines orthogonal signals with strongly correlated signals generated by space-time coding (STC) is introduced. This leads to virtual transmit antenna positions that are used to improve the unambiguous angular area of an otherwise ambiguous MIMO antenna array. The proposed method is validated with an integrated OFDM radar at millimeter-wave frequencies.

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range–azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.

Accurate and stable time and frequency transfer (TFT) is typically required in distributed sensing. While this has historically been a limiting factor in the design of netted radars, the rise of modern TFT techniques has paved the way to nanosecond level time accuracy, absolute phase synchronization, and sub-Hertz frequency accuracy. In particular, netted systems composed of mobile nodes have benefited immensely from TFT based on global navigation satellite systems (GNSS). However, GNSS systems are liable to denial of service interruption. This article demonstrates the use of a fiber-optic White Rabbit (WR) network in an operational multistatic radar—NeXtRAD, and directly compares the results to one-way multichannel GPS disciplined oscillators acting under common-view TFT.

Estimating the signal transmitted by a noncooperating radar is of great use in radar countermeasures. In this article, we propose to improve the noise resilience of the radar signal estimation by constraining the estimate to be in a low-dimensional signal space. In particular, we focus on estimating nonlinear frequency modulated radar signals that have good target resolution characteristics. A flexible 3-D odd polynomial frequency signal model is introduced as the low-dimensional signal space to compute accurate estimates at low signal-to-noise ratios (SNRs) with small number of intercepted signals. The quality of the low-dimensional signal estimates is compared with that of the estimate from the unconstrained high-dimensional space computed using principal component analysis estimator, which is the method in predominant use in the multisensor scenario. It is shown that signal estimation through low-dimensional signal spaces yields more accurate estimates at low SNRs with a smaller number of sensors compared to searching in the unconstrained high-dimensional signal space.

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.

Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: Target detection, target tracking, multipath data association, and ionospheric heights identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this article, a unified message passing (MP) approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability density function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of target kinematic state and ionospheric heights, are approximated by MF due to its simple MP update rules for conjugate-exponential models. The posterior PDFs of target visibility state and multipath data association which contains one-to-one frame (hard) constraints (i.e., at each time, a measurement is either originated from one target via a particular path or it is clutter, and each target generates at most one measurement under a particular path) are approximated by BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among latent variables. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is beneficial to improving tracking fusion performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

While a number of signal structures have been proposed for radar, frequency modulation (FM) remains the most common in practice because it is well-suited to high-power transmitters, which tend to introduce significant distortion to other waveform classes. That said, various forms of coding provide useful parameterizations for which a variety of optimization methods can be readily applied to accomplish different operational goals. To that end, the polyphase-coded FM (PCFM) implementation was previously devised as a means to bridge this gap between optimizable parameters and physically realizable waveforms. However, the original method employed to optimize PCFM waveforms involved a piecewise greedy search that, while relatively effective, was rather slow and cumbersome. Here, the continuous nature of this framework is leveraged to formulate a gradient-based optimization approach that updates all parameters simultaneously and can be efficiently performed using fast Fourier transforms, thus facilitating a general design methodology for practical waveforms that is directly extensible to myriad waveform-diverse arrangements. Results include a large number of optimization assessments to discern performance trends in aggregate and detailed analysis of specific cases, as well as both loopback and free-space experimental measurements to demonstrate practical efficacy.

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.

Subbanding technique has been applied in wideband (WB) digital array radar systems to address the challenge of high-speed signal processing and WB jamming cancellation. This article reveals that the well-established subband decomposition by polyphase subband filtering could still leave jamming decorrelation post subbanding, which would deteriorate the jamming cancellation performance. A novel implementation of the subbanding processor is then proposed by isolating digital down conversion incorporated in the subbanding operation and performing it at baseband explicitly. This method provides flexibility to process segments of the digitized spectrum, which accommodates radar receivers to contemporary requirements of frequency agility. This article proceeds to propose a joint jamming cancellation scheme for use in the complicated real-world electronic warfare scenario including both mainlobe and sidelobe jamming. The jamming cancellation performance is evaluated by simulation with a detailed investigation on jamming decorrelation mitigation and subbanding implementation consideration. More results are shown by testing the cancellation scheme on data collected with an experimental airborne WB digital array radar in a multijamming scenario toward sea surface targets.

In this article, we propose a novel method utilizing iterative semidefinite relaxation for geolocation of stationary uncooperative radars. In our scenario, the geolocation is to be performed in a receiver located on a moving platform. The proposed method uses frequency measurements that are Doppler-shifted due to the platform motion. First, constructed nonconvex maximum likelihood cost function for position estimation is relaxed to a convex optimization problem by applying linearization to range variables. Then, at each iteration of the method, carrier frequency and position of the radar are estimated jointly. Conducted experiments show that a few iterations are enough for convergence to accurate estimates. The proposed method is computationally less expensive compared to traditional techniques, which require extensive grid search procedures in either position or carrier frequency parameter space. In the experiments, the performance of the proposed method is compared to the state-of-the-art techniques and the Cramer–Rao lower bound (CRLB). It is observed that the proposed method attains the CRLB at low noise levels while still providing accurate solutions at high noise cases. Furthermore, it is also seen that platform-radar geometry has an impact on the proposed method's performance. If the radar lies in the convex hull of the receiver path, the proposed method performs significantly better due to improvement in the linearization of range values.

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.

Amplitude comparison monopulse systems encounter the target resolution problem when multiple targets occupy the same range-azimuth cell. To solve this problem, a new angle estimation approach is proposed. The traditional range-cell-based signal processing is improved by modeling the signal in the frequency domain. Thus, the range straddling loss and the range migration problem can be avoided. The closely spaced targets in the same range-azimuth cell can be well resolved by the jointly maximum likelihood estimation of the range and velocity of the targets. Then, the angle estimates are extracted by using the monopulse ratio technique with the estimated parameters of the resolved targets in the two channels. Furthermore, the RELAX algorithm is utilized to reduce the computational burden of the proposed approach. The performance of the proposed estimator is evaluated using Monte Carlo simulation.

A fast and excellent unimodular waveform with good autocorrelation and cross-correlation design method for the multiple-input multiple-output radar is devised. Unlike the existing methods that only optimize partial metrics or only optimize the short sequence in acceptable time, we propose a comprehensive waveform design method to minimize the weighted sum of almost entirely metrics under the constant modulus constraint. Then, a deep learning framework, named as the comprehensive optimization network, is derived to handle the problem. Numerical results show that the proposed method has superior performance and acceptable optimization time compared with the existing methods.

In this article, an unmanned aerial vehicles detection system is proposed for an X -band ground-based surveillance frequency modulated continuous wave Doppler radar. Two novel features are directly extracted from the Doppler processing results without a time-frequency analysis. Experimental results on measured radar echo signals show that our system consistently outperforms the state of the art in terms of detection accuracy and computational efficiency.