Non-Coherent Fusion Detection Method for Distributed MIMO Radar Based on Modified Principal Component Analysis

A distributed multiple input multiple output (MIMO) radar non-coherent fusion TMPCA detector based on modified principal component analysis (MPCA) is proposed. Firstly, for each resolution cell grid in the unified “range-azimuth-pitch” three-dimensional observation space, the data of the cells to be detected in each observation channel of the corresponding distributed radar and its reference cell data are extracted. Then, according to the false alarm probability of detection threshold, the method of iterative optimization combined with Monte-Carlo simulation is used to solve the detection threshold required for the detection of global statistics formed after the non-coherent fusion of different numbers of effective local statistics. Finally, using the detection thresholds obtained above, the detection of the entire observation space is completed by traversing all the resolved cell grids. Compared with non-adaptive filtering methods such as direct incoherent summation, taking the maximum value, and taking the sum of the first few larger values, the TMPCA detector solves the problem of solving the second threshold corresponding to the sum of different effective local statistics through a combination of iterative optimization and Monte-Carlo simulation. The practicality of the proposed detector has been demonstrated through the analysis of fusion detection performance under multiple local statistics at 2, 3, and 4 stations.