Bayesian Adaptive Detection for Distributed MIMO Radar with Insufficient Training Data

• DOI: 10.3390/electronics14010164
• Publication Date: 2025-01-03T10:02:02Z
• Authors: Hongli Li; Ming Liu; Chunhe Chang; Binbin Li; Bilei Zhou; Hao Chen; Weijian Liu

The distributed multiple-input multiple-output (MIMO) radar observes targets from different angles, which can overcome the adverse effects of target glint and avoid the situation where the target’s tangential flight cannot be effectively detected by the radar, thus providing great advantages in target detection. However, distributed MIMO often encounters a scarcity of training samples for target detection. To overcome this difficulty, this paper proposes a Bayesian approach. By modeling the target signal as a subspace signal, where each transmit–receive pair possesses a distinct and unknown covariance matrix governed by an inverse Wishart distribution, three efficient detectors are devised based on the generalized likelihood ratio test (GLRT), Rao, and Wald criteria. Comparative analysis with existing detectors reveals that the proposed Bayesian detectors exhibit superior performance, particularly in scenarios with limited training data. Experimental results demonstrate that the Bayesian GLRT achieves the highest probability of detection (PD), outperforming conventional detectors by requiring a reduction in signal-to-noise ratio (SNR). Furthermore, an increase in the degrees of freedom of the inverse Wishart distribution and the number of receiving antennas enhances detection performance, albeit at the cost of increased hardware requirements.