Distributed MIMO Radar Research Articles

Knowledge-Aided Range-Spread Target Detection for Distributed MIMO Radar in Nonhomogeneous Environments

This paper deals with the problem of detecting a moving range-spread target in distributed MIMO radar. A new knowledge-aided (KA) model that takes into account the nonhomogenous characteristics of the disturbance (clutter and noise) in distributed MIMO radar is proposed. Specifically, the disturbance covariance matrices corresponding to different transmit-receive (Tx-Rx) pairs are modeled as random matrices. These covariance matrices share a prior covariance matrix structure but with different power levels to model the nonhomogeneous clutter powers across different Tx-Rx pairs. Two cases are considered, involving either no range training (i.e., when the disturbance is highly nonhomogeneous) or some range training data. For the first case, we develop a KA generalized likelihood ratio test (GLRT) for range-spread target detection, along with a simplified version of the KA-GLRT for point-like target detection. For the second case, the KA-GLRT becomes computationally intractable, a simple ad-doc KA detector is introduced to take advantage of training data for range-spread target detection. Simulation results are presented to illustrate the performance and effectiveness of the proposed detectors in nonhomogeneous environments.

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.

Target detection in distributed MIMO radar with registration errors

In this paper, we consider a target detection problem in a distributed multiple-input multiple-output radar with registration errors. Registration errors mean the alignment errors when observation data from different distributed radars are transformed in a common coordinate system. Our study is motivated by the fact that the perfect registration condition is unavailable in practice, even after a registration process. A model for the imperfect registration is introduced where an uncertainty window centered on the cell being tested along range and azimuth in each spatial diversity channel is employed. A generalized likelihood ratio test (GLRT) detector and a Bayesian likelihood ratio test detector are developed based on the uncertainty windows. The probability of false alarm of the proposed GLRT detector is derived. For convenience of comparison, we also analyze the detection performance of the conventional likelihood ratio test (LRT) detector with registration errors. Simulation results demonstrate that the detection performance of the two proposed detectors outperform the conventional LRT detector in the imperfect registration case.

Moving Target Detection in Distributed MIMO Radar on Moving Platforms

This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals.

An Efficient Multi-target Localization Method for Distributed MIMO Radar

An Efficient Multi-target Localization Method for Distributed MIMO Radar

Cooperative Game Approach to Power Allocation for Target Tracking in Distributed MIMO Radar Sensor Networks

In this paper, power allocation in distributed multiple-input multiple-output radar is investigated for range-only target tracking such that the determinant of Bayesian Fisher information matrix (B-FIM) is maximized. First, the B-FIM is derived for a signal model that incorporates the propagation path loss, the target reflectivity, the transmitted power, and the target prior density. Then, we model the problem as a cooperative game and exploit the solution concept of the Shapley value to distribute a given power budget among all transmitting radars for target tracking. In numerical examples, it is shown that uniform power allocation is not in general optimal. We illustrate the effects of the radar network geometry configuration, target prior density and number of antenna upon the power allocation results, and further demonstrate the superior performance of the proposed optimal power allocation scheme via Monte Carlo simulations.

Distributed MIMO radar based on sparse sensing: Analysis and efficient implementation

In sparse sensing based distributed multiple-input multiple-output radars, the problem of target estimation is formulated as a sparse vector recovery problem, where the vector to be recovered is block sparse, or equivalently, the sensing matrix is block diagonal and the sparse vector consists of equal-length blocks that have the same sparsity profile. This paper derives the theoretical requirements and performance guarantees for the application of sparse recovery techniques to this problem. The obtained theoretical results confirm previous, simulations-based observations that exploiting the block sparsity of the target vector can further reduce the amount of measurements needed for successful target estimation. For signal recovery, two low-complexity approaches are proposed. The first one is an alternating direction method of multipliers-based sparse signal recovery algorithm, which in addition to significantly reducing computations is also amenable to a parallel and semidistributed implementation. The second approach decouples the location and speed estimation into two separate stages, with each stage addressing a sparse recovery problem of lower dimension while maintaining high estimation accuracy.

Space-Partition-Based Target Detection for Distributed MIMO Radar

Space-Partition-Based Target Detection for Distributed MIMO Radar

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.

Ambiguity Function Analysis for UMTS-Based Passive Multistatic Radar

There has been a growing interest in passive radar systems in the research community over the last decade because of the several merits they offer, including ease of deployment, low cost, and non-detectability of the receivers. During the same period, the idea of distributed MIMO radar and its advantages under the coherent and non-coherent operating scenarios has been extensively studied. Keeping these benefits it mind, in this paper, we consider a UMTS-based passive multistatic radar with distributed antennas. We compute the ambiguity profiles of this radar system under both the coherent and non-coherent modes. The non-coherent processing mode improves the target detection performance by obtaining spatially diverse looks of the target. On the other hand, coherent processing enhances the resolution of target localization. We use numerical examples to demonstrate our analytical results.

Antenna Selection and Placement Analysis of MIMO Radar Networks for Target Localization

A distributed MIMO radar network consists of a large number of transmitters/receivers to cover a sensing area of interest but under the constraint that the number of antennas is limited. The problem of selecting a subset of antennas at a sampling time to maximize the diversity capability of the MIMO radar in terms of target resolvability is addressed from the information geometric viewpoint in this paper. Fisher information matrix of distributed MIMO radar which takes both radar waveform and SNR into account is derived as a function of individual antenna locations. The Fisher information distance (FID) between the measurement distribution based on all antennas and that based on only the selected set of antennas is calculated and is adopted as the criterion of selecting a subset of antennas. The underlying antenna selection problem is then to select a subset of antennas from all antennas which can result in the “shortest” FID with respect to a given target location. Since an NP hard search process is involved, an exhaustive search method is considered for efficient antenna subset selection. Simulation examples are presented to demonstrate the effectiveness of the proposed algorithm compared with other criteria in different antenna configurations.

Adaptive Sparse Recovery of Moving Targets for Distributed MIMO Radar

The high resolution and better recovery performance with distributed MIMO radar would be significantly degraded when the target moves at an unknown velocity. In this paper, we propose an adaptive sparse recovery algorithm for moving target imaging to estimate the velocity and image jointly with high computation efficiency. With an iteration mechanism, the proposed method updates the image and estimates the velocity alternately by sequentially minimizing the norm and the recovery error. Numerical simulations are carried out to demonstrate that the proposed algorithm can retrieve high-resolution image and accurate velocity simultaneously even in low SNR.

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.

A closed-form expression for false alarm rate of adaptive MIMO-GLRT detector with distributed MIMO radar

In this paper, we consider the detection problem in Gaussian noise using a multiple-input-multiple-output (MIMO) radar with spatially dispersed antennas. A MIMO version of generalized likelihood ratio test detector is provided, which is referred to as MIMO-GLRT detector. A closed-form expression for the probability of false alarm of this MIMO-GLRT detector is derived. This theoretical result is confirmed with Monte Carlo simulations.

Evaluation of Covariance Matrix in Distributed MIMO Radar

Abstract It has been shown that the detection performance can be improved by exploiting the covariance matrix of the channel vector in the distributed MIMO radar. In this paper, we firstly introduce a system model in which the target is modeled as the sum of a finite number of independent scatterers without limitation on transmitter-receiver configurations. Using this system model, we make a detailed analysis of the covariance matrix under various target RCS models. The formula to calculate the elements of covariance matrix for the MIMO radar with arbitrary transmitter-target-receiver collocations is derived. We also propose an approach to evaluate correlations between channels based on the local maximum potential detection and the received signals. This approach makes it possible to predict the detection performance for the actual distributed MIMO radar. The theoretical probability of detection for this approach is developed and validated by simulation.