Station layout optimization and imaging of distributed passive radar

Due to its low imaging cost and strong robustness, the distributed passive radar had become a popular research area. Based on the distributed radar sparse imaging model, this paper proposes a distributed passive radar imaging receiver configuration optimization method, with the highest imaging resolution as the optimization objective function, and the genetic algorithm to calculate the optimal receiver for different transmitter layouts. At the same time, the Orthogonal Matching Pursuit algorithm has low imaging accuracy and low SNR, and the signal estimation is inaccurate. It is derived that the received signal is sparsely represented by covariance, and Sparse Bayesian Learning is used. An imaging algorithm for signal reconstruction is performed, and the improvement of imaging performance is verified by simulation experiments.