Distributed MIMO Two-Coordinate Radar Signal-Level Fusion Algorithm Based on Dynamic Programming

Distributed MIMO radar cooperative detection system has the advantages of anti-target RCS fluctuation and high accuracy of parameter estimation, so it has become a research hotspot for scholars at home and abroad in recent years. At present, the distributed MIMO radar cooperative detection information fusion method mainly focuses on the data level, which can effectively improve the stability of target tracking, and can't improve the detection distance of the radar. In view of the above shortcomings, this paper proposed a distributed MIMO two-coordinate radar signal-level fusion algorithm based on dynamic programming. Firstly, the orthogonal matched filtering is used for separating different channels data from the radar echo signals, and the signals among different channels received by a single radar are respectively processed, which completes the Doppler filtering, space coordinate transformation and non-coherent integration based on dynamic programming between different channels. Then, the processed result of each radar is transmitted to fusion center to further realize the secondary non-coherent integration between multiple radars by using dynamic programming and then achieve the target detection, which can greatly improve the SNR to achieve effective detection of low observable targets. Finally, the performance of the two-stages non-coherent signal-level fusion integration algorithm proposed in this paper is theoretically analyzed. Simulations show the robustness and feasibility of the algorithm.