Distributed Multiple-input Multiple-output Radar Research Articles

A novel geometric-based bistatic range grouping algorithm for multi-target localization in distributed MIMO radar systems

In this paper, a novel bistatic range grouping algorithm is proposed in order to localize multiple targets in distributed multiple-input multiple-output (MIMO) radar systems. In a distributed MIMO radar, target localization is performed based on bistatic ranges, the distances between antennas through a target, without directional information, and grouping bistatic ranges with respect to the target is a crucial step to estimate multiple targets efficiently. In contrast to the existing grouping schemes that use tentative target estimation or complicated joint bistatic range grouping and target estimation, the proposed algorithm uses only the geometric property of bistatic ranges, resulting in computationally efficient and robust grouping algorithm without noise enhancement, especially for realistic noisy environments where other existing grouping algorithms often fail. The performance of the proposed scheme is confirmed by evaluating the probability of miss detection and the probability of false alarm through numerical simulations for various environments including indirect paths and blocked paths. The root mean square error performance of the multi-target localization using the proposed bistatic grouping algorithm is also presented for realistic noisy environments.

Antenna Placement Optimization for Distributed MIMO Radar Based on a Reinforcement Learning Algorithm

This paper studies an optimization problem of antenna placement for multiple heading angles of the target in a distributed multiple-input multiple-output (MIMO) radar system. An improved method to calculate the system’s coverage area in light of the changing target heading is presented. The antenna placement optimization problem is mathematically modelled as a sequential decision problem for compatibility with reinforcement learning solutions. A reinforcement learning agent is established, which uses the long short-term memory (LSTM)-based proximal policy optimization (PPO) method as the core algorithm to solve the antenna placement problem. Finally, the experimental findings demonstrate that the method can enhance the coverage area of antenna placement and thus has reference value for providing new ideas for the antenna placement optimization of distributed MIMO radar.

Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.

Distributed MIMO Radar Adaptive Detection in the Presence of Spectral Symmetry

We derive several parametric detectors for the case of a ground-based distributed multiple-input multiple-output (MIMO) radar system with co-located transmitters and receivers. The proposed detectors are based on exact and approximate generalized likelihood ratio tests. For the transmit-receive paths which start and end at the same node, we incorporate the constraint of spectral symmetry, meaning that the clutter returns are assumed to have a power spectral density that is symmetric around the zero Doppler, which is motivated by statistical analyses performed on measured clutter radar data. Our numerical simulations suggest that the proposed parametric detectors, which incorporate the physics-based spectral symmetry constraint on clutter returns, achieve better performance than previously investigated parametric detection approaches which do not incorporate this constraint.

Multitarget Enumeration and Localization in Distributed MIMO Radar Based on Energy Modeling and Compressive Sensing

Target localization is one of the most important research topics in the field of radar signal processing. In this paper, the problem of multitarget enumeration and localization in the distributed multiple-input multiple-output (MIMO) radar with noncoherent processing mode is investigated. We first analyze the theoretical bound of the multitarget localization accuracy under the discrete time signal model and the Swerling 1 target model. It is determined by the Cram'r-Rao lower bound (CRLB) at low signal-to-noise ratio (SNR) and the sampling lower bound (SLB) when the SNR is high. Furthermore, an innovative multitarget enumeration and localization scheme is developed, which is based on the energy modeling of the multiple transmitter-receiver paths and the compressive sensing theory. To solve the sparse vector recovery issue, we design a lightweight iterative greedy pursuit algorithm including the similarity evaluation strategy. In addition, an iterative-based target position refinement process is designed to alleviate the off-grid problem caused by the spatial discretization. The proposal utilizes the samples of the raw signals and belongs to the category of the direct localization. Nevertheless, it has significantly higher computational efficiency and lower data communication burden than the conventional direct localization methods, while avoiding the complex data association encountered by the indirect localization methods. Finally, the simulation results validate the effectiveness and robustness of the proposed method.

Distributed Microwave Photonic MIMO Radar With Accurate Target Position Estimation

A distributed microwave photonic multiple-input multiple-output (MIMO) radar is presented to accurately estimate the two-dimensional (2-D) position of targets. With wavelength-division-multiplexing-based recirculating frequency shifting, mutually orthogonal broadband stepped-frequency chirp (SFC) signals can be produced simultaneously, avoiding the complex implementation of a distributed MIMO radar. Microwave photonic dechirping is also performed to process broadband echoes, while a bandwidth synthesis is carried out to fast and accurately estimate the time of arrival (TOA). Thus, 2-D localization can be obtained by a back-projection (BP) algorithm. In a proof-of-concept experiment, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> MIMO radar with a bandwidth of 18 GHz is conducted, providing a 1-D ranging error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; \pm 1$ </tex-math></inline-formula> mm and a 2-D positioning error of < 1 cm.

Performance Boosting for Two-Step Localization Methods in Distributed MIMO Radar via a Robust Post-Processing Scheme

In radar target localization using time delay (TD) measurements, reliable TD extraction is an important prerequisite for conventional two-step localization methods. In the distributed multiple-input multiple-output (MIMO) radar, the low-quality TD measurements (i.e., outliers) always occur due to the target scattering fluctuations in various transmitter-receiver paths, which results in serious performance degradation to the generic two-step localization methods. To this end, we propose a simple yet effective performance boosting scheme in this letter. It constructs a robust estimator based on the Huber loss function and solves this problem iteratively by using the majorization-minimization (MM) technique. This proposal is a general post-processing module, and thus it can be combined with any existing two-step algorithms to achieve performance improvement in the presence of outliers. The simulations verify the effectiveness of the proposed scheme by comparing with the derived theoretical lower bound. In addition, the parameter robustness and the computational burden are also evaluated.

Beam Scheduling for Early Warning With Distributed MIMO Radars

Distributed multiple-input multiple-output (MIMO) radars achieve long-range detection by steering and focusing the narrow beams of all transmit and receive antennas on a single point. This paper proposes a beam scheduling scheme based on the joint optimization of three components: arrangement of focal points, beam dwell time allocation of each focal point, and scanning order of focal points. The purpose is to enable distributed MIMO radars to provide early warning of incursions entering a broad surveillance region. To this end, a mathematical model is first developed to evaluate radar detection capability in a cell under test (CUT) over a scheduling period. By integrating all the CUTs within the region, the detection time required to reach a specified performance in terms of cumulative detection probability is then derived. On this basis, a beam scheduling strategy is devised, considering the maximum speed and minimum average radar cross-section constraints of targets to be detected. The main mechanism of the strategy is to minimize the detection time without sacrificing performance. To address the optimization problem of beam scheduling, we propose a two-stage method using a detection capability threshold as an extra optimization variable. In our method, the first stage is used to obtain optimal schemes for different given thresholds, and in the next stage, the optimal threshold is determined from these schemes. The simulation results show that the proposed algorithm is effective.

A Relaxed Energy Function Based Analog Neural Network Approach to Target Localization in Distributed MIMO Radar

Analog neural networks are highly effective to solve some optimization problems, and they have been used for target localization in distributed multiple-input multiple-output (MIMO) radar. In this work, we design a new relaxed energy function based neural network (RNFNN) for target localization in distributed MIMO radar. We start with the maximum likelihood (ML) target localization with a complicated objective function, which can be transformed to a more tractable one with equality constraints by introducing some auxiliary variables. Different from the existing Lagrangian programming neural network (LPNN) methods, we further relax the optimization problem formulated for target localization, so that the Lagrangian multiplier terms are no longer needed, leading to a relaxed energy function with better convexity. Based on the relaxed energy function, a RNFNN is implemented with much simpler structure and faster convergence speed. Furthermore, the RNFNN method is extended to localization in the presence of transmitter and receiver location errors. It is shown that the performance of the proposed localization approach achieves the Cramér-Rao lower bound (CRLB) within a wider range of signal-to-noise ratios (SNRs). Extensive comparisons with the state-of-the-art approaches are provided, which demonstrate the advantages of the proposed approach in terms of performance improvement and computational complexity (or convergence speed).

Modified OMP method for multi-target parameter estimation in frequency-agile distributed MIMO radar

Introducing frequency agility into a distributed multiple-input multiple-output (MIMO) radar can significantly enhance its anti-jamming ability. However, it would cause the sidelobe pedestal problem in multi-target parameter estimation. Sparse recovery is an effective way to address this problem, but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity. In this paper, we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars, by modifying the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed method is then verified by simulation results.

Resource Scheduling for Multitarget Imaging in a Distributed Netted Radar System Based on Maximum Scheduling Benefits.

As a new radar system with improved performance, distributed multiple-input multiple-output (MIMO) radar provides a new idea for the development of netted radar. Aiming at the limited resource allocation problem of netted radar, this paper extends the idea of distributed MIMO radar to netted radar and proposes a resource scheduling algorithm for multitarget imaging in distributed netted radar based on the maximum scheduling benefits. Under the condition of the cognition of the target characteristics, the algorithm comprehensively considers the angle and dwell time to complete the multiradar and multitarget matching. Then it uses the principle of compressed sensing to calculate the pulse resources required for sparse imaging of each target on the corresponding radar. In this paper, the scheduling benefit of a radar system is expressed by weighting the three factors of the scheduling success rate, the hit value rate and the pulse resource consumption rate. The resource scheduling model is established according to the maximum scheduling benefits and solved using a heuristic algorithm. The simulation results show that compared with the traditional algorithm, this method improves the scheduling benefits of the radar system.

Joint resource optimization for a distributed MIMO radar when tracking multiple targets in the presence of deception jamming

How to utilize the limited resource to achieve better tracking accuracy in a distributed multiple-input multiple-output (MIMO) radar plays a critical role under the electronic countermeasure (ECM) background. In this paper, a joint resource optimization scheme (JROS) is presented for transmit and receive station selection and power allocation in anti-deception jamming scenario. A quality-of-service (QoS) model of minimizing the gap between rejection probability of false target (RPFT) and the threshold is established, and the optimal discriminator is designed on the basis of predicted posterior Cramér–Rao lower bound (PCRLB) on the deception parameters. The binary selection variables lead to the nonconvexity of objective function. To overcome this difficulty, a three-step optimization cycle (TSOC)-based algorithm is proposed. Simulation results confirm the superiority of the proposed scheme, compared with state-of-the-art resource allocation strategies. The results also show that the stations closer to the jammer with lower normalized deception parameters are often activated and allocated to more power resource.

Target Localization in Asynchronous Distributed MIMO Radar Systems With a Cooperative Target

In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

Power Minimization-Based Joint Resource Allocation Algorithm for Target Localization in Noncoherent Distributed MIMO Radar System

In this article, an optimization scheme integrating power allocation, bandwidth assignment and radar node selection (PABARNS) is developed to improve power utilization efficiency (PUE) of noncoherent distributed multiple-input–multiple-output (MIMO) radar system for target localization. The basis of the proposed scheme is to minimize the total transmit power subject to a predetermined target localization accuracy requirement, where the target localization accuracy is evaluated with the Crámer–Rao lower bound. Subsequently, the PABARNS scheme results in a mixed nonlinear Boolean-nonconvex optimization problem. In view of the unique feature of the optimization model, a two-step technique based on the heuristic algorithm and alternating direction method of multipliers method is presented to solve the underlying optimization problem, with merit of that it is more workable than other existing approaches when the power and bandwidth lie in any bound set. Finally, the simulation reveals the higher accuracy and lower complexity of the developed algorithm compared with the benchmarks, as well as the superiority of the PABARNS scheme compared with over other existing schemes in the view of the achievable PUE.

Multitarget Detection Strategy for Distributed MIMO Radar With Widely Separated Antennas

In this paper, we propose a novel solution to detect multiple targets using a distributed multiple-input multiple-output (MIMO) radar under the so-called “defocused transmit-defocused receive” operating mode. The proposed method employs a grid-based data matching algorithm, aiming to associate the target responses to potential target locations, solving the resulting data puzzle that evaluates the various cells under test (CUTs) in the surveillance area resulting from the intertwined range cells across in all transmit-receive channels. Sketchily, the approach divides the surveillance area into identically interlocking and analytically expressible grid cells, and then selects the grid cells with the best fitting multi-channel data to be equivalently regarded as the CUTs. Next, the generalized likelihood ratio test (GLRT) detector is derived to test for target presence in each of the selected grid cells. A separate procedure is introduced to eliminate the spurious “shadow targets”, false alarms occurring in the grid cells without target while sharing range cells with the targets. The essence of this procedure is to find the source of the observed contributions to the grid cells whose test statistics exceed their thresholds, and simultaneously to obtain the positions of the targets. The proposed method is evaluated using both numerical simulations and experimental data recorded by five small radars, demonstrating the effectiveness of the proposed technique.

Parametric Wald test for target detection with distributed MIMO radar in partially mixing homogeneous and non‐homogeneous environments

This study focusses on the target detection problem in the distributed multiple-input multiple-output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter-receiver path and simultaneously non-homogeneous for different paths. By characterising the disturbance signals with a series of auto-regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors’ proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramér–Rao bound (CRB).

Robust Localization in Distributed MIMO Radar Using Delay and Angle Measurements with Impulsive Noise Robust TD/AOA Localization in Impulsive Noise

This paper considers target localization using time delay (TD) and angle of arrival (AOA) measurements in distributed multiple-input multiple-output (MIMO) radar. Aiming at the problem that the localization performance of existing algorithms degrades sharply in the presence of impulsive noise, we propose a novel localization algorithm based on ℓ p -norm minimization and iteratively reweighted least squares (IRLS). Firstly, the TD and AOA measurement equations are established in the presence of zero-mean symmetric α-stable noise; then, the localization problem is transformed to a ℓ p -norm minimization problem by linearizing the measurement equations; and finally, the ℓ p -norm minimization problem is solved using IRLS by which the target position estimate is obtained, and the optimal choice of norm order p is deduced. Moreover, the Cramér–Rao bound (CRB) for target position estimation in impulsive noise is also derived, generalizing the Gaussian CRB. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of localization accuracy and robustness in impulsive noise.

Target localization in distributed MIMO radars via improved semidefinite relaxation

Target localization is an important problem in distributed multiple-input multiple-output (MIMO) radar systems. In this paper, a new algorithm using bistatic range measurements is developed for target localization in distributed MIMO radars. Unlike most existing schemes, the proposed algorithm firstly applies semidefinite relaxation to convert the maximum likelihood localization problem into a convex optimization problem. Subsequently, a novel procedure is devised to improve the solution accuracy of the convex optimization problem. Our scheme exhibits evidently better threshold behavior than the state-of-the-art approaches. Moreover, it does not require any initial estimate of the target position. Simulation results verify the superiority of the proposed algorithm over various existing methods.

Optimal transmitter and receiver placement for localizing 2D interested-region target with constrained sensor regions

This paper presents a new transmitter and receiver placement method for sensing a target in a region of interest with distributed multiple input multiple output (MIMO) radar systems. Since the Cramer-Rao lower bound (CRLB) is the standard measurement for target localization accuracy, this paper divides the region of interest into grid points and formulates the transmitter and receiver placement problem via the minimax criterion. Thus we can ensure that the joint (x, y) CRLB corresponding to each grid point of the interested region is sufficiently small. To tackle such a nonconvex and nonlinear problem, we apply the alternating direction method of multipliers to divide the difficult problem into several solvable subproblems, respectively, with fewer variables. We also extend the algorithm to the separate (x, y) CRLB-based sensor placement problem and continuous distribution of uncertain target. Simulation results show that our proposed algorithms are efficient methods for transmitter and receiver placement in distributed MIMO radar systems.

Target Localization Geometry Gain in Distributed MIMO Radar

In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information matrix (FIM), which is equivalent to minimizing the error ellipse area. The square root of the average determinant of the FIM can be expressed as a product of three parameters, namely the equivalent single radar gain, coherency gain and geometry gain. It is shown that the coherency gain of coherent MIMO radar is greater than the non-coherent one, while the geometry gain of coherent MIMO radar is always smaller than or equal to the non-coherent case. The maximum value of the geometry gain for a MIMO radar system with N transmit and M receive antennas is proportional to MN for coherent while it is √2MN for the non-coherent case.