Knowledge-Aided Wald detector for Range-Extended Target in Nonhomogeneous Environments

This paper deals with the problem of detecting the moving range-extended target in the distributed MIMO radar. Firstly, the interference covariance matrices corresponding to different transmit-receive (Tx-Rx) antennas are modeled as random matrices which express nonhomogeneous environments. Then a knowledge-aided model which makes these random matrices share a prior covariance matrix structure is built to simulate the characteristics of clutter and noise in nonhomogeneous environments. On this basis, we design a new knowledge-aided Wald (KA-Wald) detector. Simulation results show that the proposed detector possesses a better detection performance compared with the traditional Wald detector. And relative to the knowledge-aided generalized likelihood ratio test (KA-GLRT) detector, the proposed KA-Wald detector has a similar detection performance but a higher efficiency.