Abstract:

When the covariance matrix is estimated with training samples contaminated by targetlike signals, the performance of target detection in multipleinput multipleoutput (MIMO) radar spacetime adaptive processing (STAP) decreases. Aiming at this deficiency, a knowledgeaided (KA) generalized inner product (GIP) method for nonhomogeneous samples detection is proposed. Firstly the clutter subspace knowledge estimated by prolate spheroidal wave functions is utilized to construct the clutter covariance matrix offline. Then the GIP nonhomogeneity detector (GIP NHD) is integrated to realize the effective selection of training samples, which eliminates the effect of the targetlike signals in training samples on target detection. The simulation results show that compared with the conventional GIP method, the KAGIP method can screen out contaminated training samples more effectively and the target detection performance of MIMO radar STAP can be improved significantly. Thus the proposed KAGIP method is more valuable for practical engineering application.