Distributed MIMO Radar Systems Research Articles

Target Detection Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Target Detection Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Target Measurement Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Target Measurement Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Detection Power of a Distributed MIMO Radar System

Detection Power of a Distributed MIMO Radar System

An antenna subset selection algorithm in distributed MIMO radar for target localization via convolutional neural network

AbstractSince the distributed MIMO radar (DMR) has widely spread transmitters and receivers, it can provide higher target detection probability, as well as superior target tracking and localization performance than the monostatic/bistatic radar systems. An effective radar resource allocation scheme can optimise the DMR system parameter and obtain better system performance. In this paper, a critical but limited system resource is optimised, that is, select a subset of the active radar antennas. In this scenario, the convolutional neural network of the antenna subset selection for target localization (LCNN‐ASS) algorithm in the DMR is proposed based on two free switching policies. The proposed algorithm is immune to the failure of a single policy and selects antenna subsets from the entire sets with a remarkable computational speed. Therefore, the proposed algorithm increases the flexibility of resource scheduling over traditional algorithms. Simulation experiments and performance analysis demonstrate the localization performance and flexibility of the LCNN‐ASS algorithm.

LPI-Based Transmit Resource Scheduling for Target Tracking with Distributed MIMO Radar Systems

This paper explores the effect of the low probability of intercept (LPI) performance optimization on target tracking in distributed MIMO (D-MIMO) radar systems, where an intercept receiver is equipped on the target. The aim of the proposed LPI-based transmit resource scheduling (LPI-TRS) strategy is to optimize the LPI performance of the D-MIMO systems meanwhile satisfy the target tracking accuracy requirement by the proper transmit resource management design. Different from the existing research, we introduce a cascading mathematical model to describe the intercept process with the specific intercept receiver. A cascading product of the probabilities of interception and detection deduces the probability of report <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> for the interceptor, to evaluate the LPI performance of one transmit radar node. Accordingly, the maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> is employed, for the overall D-MIMO radar systems, to perform as the global LPI performance metric. Based on the min-max criterion, by minimizing the global LPI metric, a novel non-convex optimization problem is formulated wherein the requirements for target tracking performance and the system resource limitation are considered as the constraints. Wherein the tracking task is fulfilled by the two-tier hybrid processing framework. Meanwhile, we introduce a convex relaxation-based approach, so the sub-optimal results are acquired via a two-step fast computational search algorithm. Subsequently, the closed-loop scheme is set up to achieve LPI-based resource awareness in D-MIMO radar systems. Several numerical results demonstrate the theoretical analysis and show that the proposed LPI-TRS leads to significant LPI performance improvements.

Moving Target Detection Using a Distributed MIMO Radar System With Synchronization Errors

Moving Target Detection Using a Distributed MIMO Radar System With Synchronization Errors

Direct Target Localization With Quantized Measurements in Noncoherent Distributed MIMO Radar Systems

In this paper, a direct target localization algorithm with quantized measurements for non-coherent MIMO systems is proposed. In this system, each receiver transmits a low-bit quantized version of the echo rather than the full raw data to the fusion center. We construct a joint likelihood function based on the low-bit data of each receiver, from which the unknown target position can be directly determined. The Cramer-Rao lower bound (CRLB) is derived to analyze the localization performance of our proposed algorithm. To maximize the localization performance, a CRLB-based objective function is designed to obtain the optimum quantization thresholds. The formulated problem is a high-dimensional and non-convex optimization problem that when solved, determines two types of coupled parameters for the quantization thresholds and the complex-valued scaling coefficients of the signal. We propose a batch gradient descent embedded particle swarm optimization algorithm to solve this problem effectively. Numerical results show that the proposed algorithm delivers superior performance in terms of maximizing the overall localization performance, and the 3-bit quantized algorithm is able to provide performance that is very close to the unquantized algorithm. Experimental data recorded by three small radars are also provided to demonstrate the effectiveness of the proposed algorithm.

An Approximately Efficient Estimator for Moving Target Localization in Distributed MIMO Radar Systems in Presence of Sensor Location Errors

In this paper, a novel algebraic closed-form method is proposed to estimate the position and velocity of a moving target in distributed multiple-input multiple-output radar systems with erroneous sensor locations by utilizing time delay and Doppler shift measurements. Unlike the existing methods that introduce nuisance parameters to build the pseudo-linear equations, the proposed method uses the singular value decomposition approach to establish new linear equations with regard to the target location with no nuisance parameters, and then derives a weighted least-squares (WLS) solution. To further improve the localization accuracy, the solution is refined through estimating the error by another WLS estimator. Based on the theoretical derivation and numerical simulations, the proposed estimator is demonstrated to be approximately unbiased and can attain the CRLB under small noise conditions. Moreover, the simulation results show that the proposed method achieves better target location accuracy than the state-of-the-art algorithms.

A Novel TSWLS Method for Moving Target Localization in Distributed MIMO Radar Systems

In this letter, the moving target localization problem is studied under distributed multiple-input multiple-output radar systems. Based on the two-stage weighted least squares framework, a closed-form solution of target position and velocity is derived. In the first stage, linear equations about target position and velocity are established with no nuisance parameters. Then the estimate is achieved by a weighted least squares (WLS) estimator. In the second stage, the estimation error of the first stage is estimated by another WLS method to refine the localization performance. The final estimate, combining solutions of the first stage and the second stage, is approximately unbiased and its covariance is equal to the Cramer-Rao lower bound under small noise conditions through the theoretical derivation and numerical simulations. Simulation results demonstrate the proposed method achieves better location accuracy than the state-of-the-art algorithms.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

Parametric Adaptive Matched Filter for Multistatic MIMO Radar

A fully distributed MIMO radar system can be treated in terms of all bistatic pairs. If a bistatic pair in a distributed MIMO radar system employs multiple transmit and receive elements in a phased array configuration, this increases the dimensionality of the data received over a coherent processing interval, which in turn increases the training data needed to reliably estimate the covariance matrix. This problem is exacerbated by the heterogeneity of training data arising from the bistatic geometry, which further degrades the quality of the covariance matrix estimate used for adaptive detection, resulting in a deleterious impact on space-time adaptive processing performance. To address these issues, we develop a parametric approximation to a physics-based clutter model, which approximates the physical characteristics of a variety of different real world clutter environments, and is especially useful in modeling clutter scenarios encountered in distributed MIMO radar. We show that clutter can be approximated as a multichannel autoregressive process of model order 4. This allows us to reduce the training data requirements and minimize the effects of training data heterogeneity on the detection performance. We demonstrate our results through simulation.

Exact Solution for Elliptic Localization in Distributed MIMO Radar Systems

Elliptic localization is a range-based positioning technique exploiting multiple transmitter-receiver pairs, each of which provides separate bistatic range (BR) measurements. In this paper, a novel computationally efficient solution for locating a single target from BR measurements in distributed MIMO radar systems is proposed. Due to nonconvex nature of the associated maximum likelihood (ML) estimation problem, its globally optimal solution is difficult to obtain. We first reformulate the ML estimation as a nonconvex constrained weighted least squares problem. Owing to special structure of the resulting problem, it is recast as a convex problem, whose exact solution can be obtained in closed-form or near closed-form manner. Moreover, the proposed method is extended to localization in the presence of antenna location uncertainties. The positioning performance of the proposed method is shown to achieve the CRLB up to relatively high noise levels. Furthermore, numerical simulations demonstrate a significant performance improvement of the proposed method over the state-of-the-art algorithms.

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation mean-square error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluated. Simulation results indicate that the proposed MFMLS and MFMLS_OAT algorithm outperform the existing algorithms. Moreover, the MFMLS algorithm can distinguish targets with different priorities, while the MFMLS_OAT algorithm can perform the tracking tasks with higher accuracy.

An Explicit Solution for Target Localization in Noncoherent Distributed MIMO Radar Systems

This work focuses on the moving target localization problem in the noncoherent multiple-input multiple-output radar system with widely separated antennas. We assume that the time delay and Doppler shift between each transmit/receive element pair have already been measured by a preprocessing algorithm. Utilizing these measurements, an explicit method for jointly estimating the target position and velocity is proposed. It first divides the measurements into several groups based on the different transmitter elements or receive elements, and then employs two best linear unbiased estimators successively for each group to independently produce an estimate of target position and velocity. Finally, these results from different groups are combined to form a composite estimate. Simulation results show that the estimated accuracy of the proposed method achieves the Cramer-Rao lower bound at sufficiently small noise conditions.

Real-Time MIMO Channel Sounder for Emulation of Distributed Ultrawideband Systems

This paper introduces an ultrawideband (UWB) channel sounding system. Its novel architecture allows real-time measurements of multiple time-variant radio propagation channels in different ultrawide frequency bands. Its architecture allows emulation of multiuser systems, sensor networks, localization systems, and distributed MIMO radar systems. The sounder uses a maximum length binary sequence (MLBS) excitation signal and correlation processing in the receiver. Its synchronous multichannel operation is supported by excellent timing stability and low power consumption of miniature size modules based upon custom integrated SiGe circuits. The paper describes the architecture, design, calibration, basic parameters, and application examples of the sounding system.