Distributed Multiple-input Multiple-output Radar Systems 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.

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.

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 and Clock Refinement in Distributed MIMO Radar Systems With Time Synchronization Errors

Achieving accurate clock synchronization may not be feasible for distributed multiple-input multiple-output (MIMO) radar systems with non-ideal clocks of the sensors. In this paper, the problem of target localization and clock synchronization in distributed MIMO radar systems in presence of time synchronization errors is addressed by utilizing time delay measurements. Firstly, the necessity to consider time synchronization errors when locating a target is illustrated by analyzing the increase in Cramer-Rao lower bound (CRLB) of the target position due to the time synchronization error. Then, a self-synchronized localization framework based on the hybrid maximum likelihood and maximum a posteriori estimator is proposed to estimate the target position and time synchronization error sequentially. Specifically, a method based on the levenberg marquardt algorithm and an analytical solution are developed respectively to estimate the target position, and a combination strategy is recommended further based on the two methods to achieve a more accurate target position estimate. The time synchronization error is figured out then by utilizing the target position estimate. Meanwhile, the closed-form solution of estimating the target position and time synchronization error is analytically proved to be approximately unbiased and can attain the CRLB under weak noise conditions. It is demonstrated through numerical simulations that the combination strategy has better estimation performance than the two methods. Simultaneously, simulation results show that the proposed self-synchronized localization method outperforms the state-of-the-art algorithms in the target position and time synchronization error estimation accuracy.

Target Localization Using Double-Sided Bistatic Range Measurements in Distributed MIMO Radar Systems.

We develop a novel approach improving existing target localization algorithms for distributed multiple-input multiple-output (MIMO) radars based on bistatic range measurements (BRMs). In the proposed algorithms, we estimate the target position with auxiliary parameters consisting of both the target–transmitter distances and the target–receiver distances (hence, “double-sided”) in contrast to the existing BRM methods. Furthermore, we apply the double-sided approach to multistage BRM methods. Performance improvements were demonstrated via simulations and a limited theoretical analysis was attempted for the ideal two-dimensional case.

A joint resource allocation method for multiple targets tracking in distributed MIMO radar systems

In order to simultaneously improve system performance and resource utilization of distributed multiple-input multiple-output (MIMO) radar systems, a joint resource allocation method is proposed to address the velocity estimation problem for multiple targets tracking in this paper. The paper focuses to improve the tracking performance for key targets using the remaining resources when the general targets have obtained resources to reach to tracking requirements. Firstly, a criterion minimizing the velocity estimation mean square error (MSE) for a key target is considered. Restricted by limited and relatively sufficient system resources and given velocity estimation requirements for general targets, a joint resource allocation optimization model with transmitters, receivers, transmitted power, and signal time is established. We propose a suboptimal method to approximately solve this problem. The method separates the optimization into three steps, where each step transforms the corresponding mixed-Boolean optimization problem into a second-order cone programming (SOCP) problem by convex relaxation. Finally, the approximately optimal solution can be obtained by cyclic minimization method. Extensive simulations indicate that compared with other methods, the proposed joint method can achieve the lowest velocity estimation MSE with the fewest transmitters. Meanwhile, limited by the given velocity estimation MSE, the proposed method can focus on the key target and achieve the whole velocity estimation error minimization while a greater flexibility for target tracking number can be obtained. Moreover, random experiments can further validate and evaluate the proposed method’s effectiveness and traceability with the given scenario.

Dynamic system for reference signal transmission and reconstruction in distributed MIMO radars

Reference signals (RSs) transmission and reconstruction are crucial in distributed multiple-input multiple-output (MIMO) radar systems. Common methods rely considerably on RF cable, optical fiber or laser, which are confined by the weak mobility, restricted visual distance or the limited bandwidth. Besides, broadcast transmitter is also widely used, but its inherent nature makes the systems more prone to security threats in electronic countermeasures (ECM). To tackle this problem, we consider radar RSs as part of a nonlinear network and propose a novel dynamic system solution to achieve waveform transmission and reconstruction in distributed MIMO radar systems. Through designing the nonlinear feedback control function properly, the RSs are reconstructed directly and precisely from the broadcast compressive signals. For those who cannot access to the priori information, it is extremely hard to complete signal reconstruction, indicating the system is of significant security in ECM. The simulations agree well with application requirements, proving the performance of the proposed solution.

Closed-Form Localization for Distributed MIMO Radar Systems Using Time Delay Measurements

This paper presents two closed-form localization algorithms, a general algorithm and a colocated algorithm, for distributed multiple-input multiple-output (MIMO) radar systems. In distributed MIMO radar systems, range sum measurements are used to estimate the location parameter. For this, the range sum error minimization is actually employed to be degenerated into two cases for time-of-arrival (TOA) passive localization, one by employing the distance estimate between the target and the receiver (the general algorithm) and the other by subtracting the distance measurement between the target and the transmitter after the time-delay estimation (the colocated algorithm). The resulting positioning accuracy of the general and colocated techniques is found to perform better than that of the existing closed-form weighted least squares (WLS) algorithm and attain the Cramér-Rao lower bound (CRLB).

MIMO radar signal design to improve the MIMO ambiguity function via maximizing its peak

One of the important obstacles in MIMO (Multiple Input Multiple Output) radars is the issue of designing proper transmit signals. Indeed, the capability of signal design is a significant advantage in MIMO radars, through which, the system can achieve much better performance. Many different aspects of this performance improvement have been considered yet, and the transmit signals have been designed to attain such goal, e.g., getting higher SNR or better detector׳s performance at the receiver. However, an important tool for evaluating the radar׳s performance is its ambiguity function. In this paper, we consider the problem of transmit signal design, in order to optimize the ambiguity function of a distributed MIMO radar system via maximizing its peak.

Space-Partition-Based Target Detection for Distributed MIMO Radar

We address the radar target detection problem for distributed multiple-input multiple-output (MIMO) radar whose diversity receiving channels may receive partially correlated target returns. Based on a method to estimate the target correlation coefficient, we can estimate the detection probabilities of the coherent accumulation detector and noncoherent accumulation detector. Based on their detection probability estimates, the surveillance space of a distributed MIMO radar system is then partitioned into two sections, one using the coherent accumulation detector, and the other using the noncoherent accumulation detector. The detection performance of the algorithm is verified via numerical results.