Distributed MIMO Radar Research Articles

Joint Antenna Scheduling and Power Allocation for Multi-Target Tracking under Range Deception Jamming in Distributed MIMO Radar System

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

Reducing On-Board Interference and Angular Ambiguity Using Distributed MIMO Radars in Medium-Sized Autonomous Air Vehicle

Reducing On-Board Interference and Angular Ambiguity Using Distributed MIMO Radars in Medium-Sized Autonomous Air Vehicle

Compound Jamming Recognition via Contrastive Learning for Distributed MIMO Radars

Compound Jamming Recognition via Contrastive Learning for Distributed MIMO Radars

Target Detection Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Target Detection Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

Target Measurement Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Target Measurement Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Robust Elliptic Positioning via Sparse Regularization and ADMM for a Distributed MIMO Radar in the Presence of Outliers

Robust Elliptic Positioning via Sparse Regularization and ADMM for a Distributed MIMO Radar in the Presence of Outliers

Calibration of Distributed MIMO Radar Systems

Calibration of Distributed MIMO Radar Systems

Robust Target Localization in Distributed MIMO Radar With Nonconvex ℓp Minimization and Iterative Reweighting

Robust Target Localization in Distributed MIMO Radar With Nonconvex <i>ℓ_p </i> Minimization and Iterative Reweighting

Optimal Polarization Design for Distributed MIMO Radar Networks: A Stackelberg Game Perspective

Optimal Polarization Design for Distributed MIMO Radar Networks: A Stackelberg Game Perspective

Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms With Imperfect Auto- and Cross-Correlation

Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms With Imperfect Auto- and Cross-Correlation

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

Fusion detection in distributed MIMO radar under hybrid-order Gaussian model

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity.

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.

Joint Selection of Transceiver Nodes in Distributed MIMO Radar Network with Non-Orthogonal Waveforms

Joint Selection of Transceiver Nodes in Distributed MIMO Radar Network with Non-Orthogonal Waveforms

Energy-Guided Multitarget Detection and Localization for Distributed MIMO Radar: Analysis and Solution

This article investigates the multitarget detection and localization problem for distributed multiple-input multiple-output radar. We first derive the optimal approach of this issue in the high-dimensional space according to the generalized likelihood ratio test (GLRT) and the maximum likelihood estimation. Moreover, the theoretical multitarget localization accuracy under the considered discrete time signal model is analyzed by invoking the Cram$\acute{\text{e}}$r–Rao lower bound and the sampling lower bound. To deal with the high complexity problem of the optimal solution, an energy-guided framework including two modules is proposed to detect and locate multiple targets. In the first module, the energy accumulation approach based on the GLRT is employed for multitarget detection, and a fast energy accumulation strategy in terms of the far-field case is developed to improve the computational efficiency. In the second module, an innovative iterative similarity evaluation method is designed to determine the positions of multiple targets by exploiting the relationship between the energy accumulation characteristics and the multitarget existence conditions. The effectiveness of the proposed framework is verified via extensive simulations in both far-field and near-field cases.

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.