POSITIVE DEFINITE MATRIX SPACE BASED DETECTOR WITH LIMITED TRAINING SAMPLES FOR MULTIPLE TARGET SITUATIONS

Multiple target situation is a typical situation of nonhomogeneous clutter environment, which can cause excessive target masking in radar signal detection system. In order to reduce target masking caused by multiple target situations, this paper proposes a new detection structure based on positive-definite matrix space and limited training samples. The proposed detection structure uses a positive-definite matrix to estimate the background power level. In addition, with limited training samples, the detection structure is used to resist the multiple target situations. The simulation results show that the proposed detection structure exhibits a better detection performance than that of the well-known CA-CFAR in homogeneous environment. The detector also performs robustly in multiple target situations, even though 10 interfering targets exist in a length of 24 samples of reference window. Furthermore, the measured results validate the performance of the proposed method.