Low-Complexity Multitarget Detection and Localization Method for Distributed MIMO Radar

Abstract

Direct position determination (DPD) for multiple targets in distributed multiple-input multiple-output (MIMO) radar has been a challenging problem. This article proposed a low-complexity multitarget detection and localization method for distributed MIMO radar. To address the problem of exponential expansion of the state space caused by high-dimensional detection in traditional DPD, a low-dimensional detector is proposed. Specifically, we divide the radar-sensed scene into discrete 2-D grid cells and derive the maximum likelihood estimation (MLE) function as well as the generalized likelihood ratio test (GLRT) detector in the 2-D scene. In addition, the probability of a false alarm (PFA) for the derived GLRT detector has an analytic solution, ensuring each grid cell maintains a constant PFA. Since the proposed detector introduces a large number of false targets, we further propose the clean with protected cells (CPCs) algorithm to remove false targets and localize real targets. This method generates protection points based on the relationship between the real targets and the radar channels, achieving high-accuracy localization with low computational complexity, even in scenes with inseparable targets. Finally, both numerical simulations and experimental data demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method achieves the best detection performance compared to state-of-the-art methods, with an average processing time of only 565.7 ms, meeting the requirements for real-time target detection and localization.