Multiple-input Multiple-output Radar Detection Research Articles

Domain Knowledge-Assisted Deep Reinforcement Learning Power Allocation for MIMO Radar Detection

The power allocation of multiple-input multiple-output (MIMO) radars is a key point in target tracking and detection. The optimality of a multitarget and multiconstraint optimization problem strictly depends on a priori model, which is difficult to obtain in time-varying, complex, and noncooperative environments. Recently, deep reinforcement learning (DRL) has been applied for target tracking tasks, which provides a trial-and-error interactive learning mechanism to improve the policy. Unlike tracking tasks with complete target state transition models, it remains an open issue for DRL-based MIMO radar detection that requires efficiently adapting the control policy to the environment of randomly appearing targets and extensive power transmission actions, which leads to sparse final task rewards and hence slow policy learning for the agent. Through introducing both the analytic model (radar equation) and empirical rules (expert preferences) for domain knowledge, this article proposes a domain-knowledge-assisted DRL (DKADRL) framework in which a domain-knowledge-based timely reward generator is utilized to generate timely rewards that assist the agent’s policy learning. To adjust the role of the timely rewards and the final task rewards, a reward fusion module is designed, which gradually increases the role of the final task rewards as the training process progresses, thus allows agent’s policy to converge to the final optimization goal. The algorithm is validated under two target motion scenarios, showing the higher target detection probability and the faster training speed, compared to equal power allocation and proximal policy optimization (PPO)-based power allocation.

One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis

Target detection is an important problem in multiple-input multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao’s test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector; 2) it allows us to handle sign measurements straightforwardly; 3) there are closed-form approximations of the detector’s distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate 2 dB loss in the low-signal-to-noise-ratio (SNR) regime compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results.

Deep MIMO radar target detector in Gaussian clutter

We focus on the target detection problem of the multiple-input multiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deep neural networks (DNNs) to improve the detection performance in a data-driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL-based MIMO radar target detection framework, a deep convolutional neural network (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.

Receive filter design for MIMO radar with one-bit ADCs

Low-resolution analog-to-digital converter (ADC), especially one-bit ADCs, is of great importance for large-scale systems due to its significant advantages, e.g., low hardware cost and low power consumption. In this paper, the problem of receive filter design for multiple-input multiple-output (MIMO) radar with one-bit ADCs is investigated. With deterministic noise distribution parameters and target scattering coefficient, the probabilities of detection and false-alarm for MIMO radar with one-bit ADCs are derived, and then the receive filter is obtained by maximizing the probability of detection subject to a fixed false-alarm. In addition, we define a quantized signal-to-noise ratio (QSNR) for MIMO radar with one-bit ADCs, and evaluate the performance difference between one-bit and infinite-bit quantizations from the SNR point of view. For imperfect scenario where the noise distribution parameters are unknown, a one-bit observation based method is devised to estimate the mean and variance of the noise. Moreover, we study the effect of unknown target scattering coefficient on the performance of the designed receive filter in terms of the QSNR, and propose robust methods to overcome this uncertainty. Finally, several representative simulations are presented to demonstrate the effectiveness of the proposed methods.

24 GHz FMCW MIMO Radar for Marine Target Localization: A Feasibility Study

Radar detection and tracking of targets in the marine environment are common tasks performed to ensure the safe navigation of ships or monitor traffic in harbor areas. More recently, radar technology has been proposed to support the collision avoidance system of autonomous surface vehicles, which are characterized by severe constraints in terms of payload and space. The paper investigates the performance of a small and lightweight 24 GHz Frequency Modulated Continuous Wave (FMCW) Multiple-Input Multiple-Output (MIMO) radar, originally developed for automotive applications, to localize marine targets at short range. A complete signal processing strategy is presented combining MIMO radar imaging, detection, and tracking algorithms. The validation of the proposed signal processing chain is firstly performed thanks to numerical tests based on synthetic data. After, results of experimental trials carried out in the marine environment are reported. These results demonstrate that the considered radar together with the adopted signal processing strategy allows the localization of static targets and the tracking of moving targets with satisfactory performance, thus encouraging its use in marine environments.

Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces

A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications.

Transmit Antenna Placement Based on Graph Theory for MIMO Radar Detection

Optimal antenna placement for target detection using multiple-input multiple-output (MIMO) radar system with widely separated antennas is studied in this paper. Considering the problem of placing a limited number of transmit antennas in a given space to maximize the detection performance, an optimization problem is formulated with the output signal-to-noise ratio (SNR) indicator as the performance metric. The initial problem is a nonlinear integer programming problem, and with the increase of the number of antennas, the computational complexity using traditional methods can be huge. In this paper, we discretize the feasible space into several grid points and employ graph theory to handle the problem. The antennas and grid points can be viewed as vertices in a bipartite graph, so that the antenna placement problem can be viewed as seeking for a proper matching between two vertex sets. We transform the problem into a stable matching (SM) problem and a maximum weighted matching (MWM) problem respectively, and propose Gale-Shapley (GS)-based and Kuhn-Munkres (KM)-based algorithms to deal with them with polynomial complexity. Numerical examples are provided to evaluate the effectiveness of the proposed methods.

An Applied Ambiguity Function Based on Dechirp for MIMO Radar Signal Analysis

The orthogonal waveform design and good hardware realization of multiple-input–multiple-output (MIMO) radar have always been important research topics. The orthogonal frequency division multiplexing (OFDM) chirp waveform has received more attention because of its large time-bandwidth product, constant modulus, no range-Doppler coupling, good orthogonality, and good Doppler tolerance. Dechirp technique can reduce the amount of raw sampled data very well in near-field miniature lightweight MIMO radar detection and synthetic aperture radar (SAR) imaging. However, most of the current waveform analysis methods are based on matched filtering (MF). In this letter, an ambiguity function (AF) based on the dechirp signal processing approach to analyze the waveform performance is proposed, called dechirp ambiguity function (DAF). The pulse compression performance of the waveform itself and the level of mutual interference between the waveforms are described from the perspective of DAF. Numerical results validate reliability and effectiveness of the DAF.

Passive MIMO Radar Detection with Unknown Colored Gaussian Noise

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.

Passive MIMO radar detection in the presence of clutter or multi‐path without reference channel

Target detection problem in a multiple-input multiple-output (MIMO) radar which includes non-cooperative illuminators of opportunity (IOs) and spatially distributed receivers is discussed. The receivers utilize only one processing channel and discard the difficulties in providing an ideal version of the transmitted waveforms. The signal model, in addition to the receiver noise and the possible echo from the target of interest, considers the direct-path and clutter/multi-path interferences (DPI and MPI). A Rao test and an approximated GLR test detectors are proposed to solve the problem and an innovative recursive algorithm is proposed to simultaneously estimate the nuisance parameters in the test. Then, by regarding some matrix properties in the proposed Rao detector expression, a low complexity version of the Rao detector is proposed. Besides, two detectors based on Rao and GLR principles in the case of active MIMO radar are introduced as the performance upper-bounds for the proposed detectors. The convergence and suitable performance of the proposed detectors are verified by numerical simulations.

Co-Located MIMO Radar Target Detection in Cluttered and Noisy Environment Based on 2D Block Sparse Recovery

In the co-located multiple input multiple output (MIMO) radar, the matched filter receiver suffers from high range sidelobes, since the waveforms originating from different transmitters cannot be fully orthogonal. Traditional sparse recovery based receivers can suppress the sidelobes by assuming the sparsity of range profiles. However, in most practical situations, the range profile is not sparse due to the presence of clutter. In this paper, an effective co-located MIMO radar target detection algorithm is proposed based on modified smoothed-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm sparse recovery, by exploiting the 2D block sparsity of the range-Doppler profiles of targets corresponding to different transmit-receive pairs. The proposed method can enhance the target detection performance in co-located MIMO radar system, especially for those weak targets which may be overwhelmed by high-level sidelobes of strong targets and clutters, without sacrificing computational efficiency. With a well-designed regularization approach and an adaptive regularization parameter update technique, the proposed method is also robust in noisy environment. Simulation and experimental results validate the effectiveness of the proposed method.

Improving Detection Performance of Passive MIMO Radar by Exploiting the Preamble Information of Communications Signal

In passive multiple-input multiple-output (MIMO) radar, the transmit signals of the noncooperative illuminators of opportunity are usually not completely known. They are usually standard communications signals following specific protocols which stipulate the preamble format. In this article, we show that the preamble information can be exploited to improve the performance of passive MIMO radar. We have derived two generalized likelihood ratio tests (GLRTs) for passive MIMO radar detection, one for the case where the noise variance is known, and the other for the case where the noise variance is unknown. Our analysis shows that the derived two GLRTs have constant false alarm rate. Simulation results show that under the same condition the derived GLRTs outperform the GLRTs without using the preamble information. Moreover, the derived GLRTs are also compared with the GLRTs for active MIMO radar that totally knows transmit signals. These comparisons show that the performance of the derived GLRTs vary between the GLRTs for active MIMO radar and those for the passive MIMO radar with the transmit signals completely unknown.

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.

Passive MIMO radar detection exploiting known format of the communication signal observed in colored noise with unknown covariance matrix

In passive multiple input multiple output (MIMO) radar, the transmit signals of the noncooperative illuminators of opportunity are usually not completely known. However, they are often standard communication signals with some specific signal structure. Exploiting such information, the detection performance of passive MIMO radar can be improved. In this paper we derive a generalized likelihood ratio test (GLRT) for passive MIMO radar detection when the covariance matrix of the colored Gaussian noise is unknown and the structure of the transmit signal is known but it contains some unknown information bits. Moreover, a model which employs a reasonable approximation for some practical scenarios while requiring only a limited number of training samples is also considered, and a GLRT for this model is also derived. Our tests are shown to be constant false alarm rate (CFAR), and their performance approaches the optimum performance with known signal structure and known covariance matrix when the number of training samples is increased. Finally, several numerical examples are presented to demonstrate the effectiveness of the proposed method.

Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

In this article, the problem of simultaneously detecting and localizing multiple targets in homogeneous noise environment is considered for non-coherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming that the a prior knowledge of target number is available, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching the parameters of interest in a high-dimensional state space. However, the complexity of this solution increases exponentially with the number $G$ of targets. Besides, if the number of targets is unknown, a multi-hypothesis testing strategy to verify all the possible hypotheses on target number is required, which further complicates this method. In order to devise computationally feasible methods for practical applications, we split the high-dimensional maximization into $G$ disjoint sub-optimization problems by sequentially detecting targets and then clearing their interference for the subsequent detection of remaining targets. In this way, we further propose two fast and robust suboptimal solutions which allow to trade performance for a much lower implementation complexity. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show that the proposed algorithms can correctly detect and accurately localize multiple targets even when targets lie in the same range bins. Experimental data recorded by three small radars are also provided to demonstrate the efficacy of the proposed algorithms.

Constant-Modulus-Waveform Design for Multiple-Target Detection in Colocated MIMO Radar.

For improving the performance of multiple-target detection in a colocated multiple-input multiple-output (MIMO) radar system, a constant-modulus-waveform design method is presented in this paper. The proposed method consists of two steps: simultaneous multiple-transmit-beam design and constant-modulus-waveform design. In the first step, each transmit beam is controlled by an ideal orthogonal waveform and a weight vector. We optimized the weight vectors to maximize the detection probabilities of all targets or minimize the transmit power for the purpose of low intercept probability in the case of predefined worst detection probabilities. Various targets’ radar cross-section (RCS) fluctuation models were also considered in two optimization problems. Then, the optimal weight vectors multiplied by ideal orthogonal waveforms were a set of transmitted waveforms. However, those transmitted waveforms were not constant-modulus waveforms. In the second step, the transmitted waveforms obtained in the first step were mapped to constant-modulus waveforms by cyclic algorithm. Numerical examples are provided to show that the proposed constant-waveform design method could effectively achieve the desired transmit-beam pattern, and that the transmit-beam pattern could be adaptively adjusted according to prior information.

Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter

For collocated Multiple-Input Multiple-Output (MIMO) radar, we investigate the target detection problem in Gaussian clutter with an unknown but random covariance matrix. An inverse complex Wishart distribution is chosen as prior knowledge for the random covariance matrix. We propose two detectors in the Bayesian framework based on the criteria of the Generalized Likelihood Ratio Test. The two main advantages of the proposed Bayesian detectors are as follows: (1) no training data are required; and (2) a prior knowledge about the clutter is incorporated in the decision rules to achieve detection performance gains. Numerical simulations show that the proposed Bayesian detectors outperform the current commonly used non-Bayesian counterparts, particularly when the sample number of the transmitted waveform is small. In addition, the performance of the proposed detector will decline in parameter mismatched situation.

Limited-Complexity Receiver Design for Passive/Active MIMO Radar Detection

In this paper, we develop efficient methods for devising lower complexity receivers that can achieve performance close to the full complexity receivers for passive/active multiple-input multiple-output (MIMO) radar. The method employed eliminates some parts of the test statistic to lower either hardware or software complexity. For the case of spatially uncorrelated reflection coefficients and spatially white clutter-plus-noise, the test statistic requires the computation of a set of matched filters, each matched to a signal from a different transmitter. In this case, our method is equivalent to selecting a specific set of transmitters to provide optimum performance. In the more general case of correlated clutter-plus-noise and reflection coefficients, then the test statistic requires the computation of a larger set of matched filters. These matched filters correlate the clutter-plus-noise free signal received at one receive antenna due to the signal transmitted from some transmit antenna and the signal received at another receive antenna. In the more general case, our algorithm picks the best of these matched filters to implement when the total number of these matched filters one can implement is limited.

OFDM Chirp Waveform Design Based on Subchirp Bandwidth Overlap and Segmented Transmitting for Low Correlation Interference in MIMO Radar

There are some special merits for the orthogonal frequency division multiplexing (OFDM) chirp waveform as multiple input multiple output (MIMO) signals. This signal has high range resolution, good Doppler tolerance, and constant modulus superiority since it exploits a full bandwidth and is based on chirp signals. The correlation sidelobe peaks level are critical for the detection requirement of MIMO radar signals, however conventional OFDM chirp signals produce high autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP), which reduces detection performance. In this paper, we explore the structure of OFDM chirp signals’ autocorrelation function and proposed a scheme to reduce the designed signal’s ASP by a designing suitable range of subchirp bandwidth and a segmented transmit-receive mode. Next, we explore a suitable range of interval between the chirp rates of each two signals to reduce the CP. The simulation of designed signals verifies the effectiveness of the proposed methods in the reduction of ASP and CP, with the correlation performance being compared with recent relate studies. In addition, the multiple signals detection and one-dimensional range image simulation show the good detection performance of a designed signal in MIMO radar detection.

Knowledge‐aided detection for airborne MIMO radar by exploiting structured clutter spectrum

In this study, the authors propose two knowledge-aided detection schemes based on prior structured clutter information for airborne collocated multiple-input multiple-output (MIMO) radars. Based on a first-order representation of the clutter spectrum, they propose the first-order generalised likelihood ratio test (FO-GLRT) detector. Meanwhile, Based on a second-order representation of the clutter spectrum, they propose the second-order GLRT (SO-GLRT) detector. They also analyse and compare the detection performance of those two detectors in different situations. The FO-GLRT detector can achieve acceptable performance without any secondary data, and thus immune to the clutter heterogeneity. The SO-GLRT detector that exploits the structured clutter covariance matrix is able to achieve a nearly optimal performance in certain cases. Both the proposed detectors are suitable for the collocated MIMO radars, which have a high signal dimension and a large demand for independent identically distributed training samples. Simulations validate those results.