Cyclic training sample selection and cancellation technique for airborne STAP radar under nonhomogeneous environment

Abstract

When training samples contain interference target signals (i.e., outliers), the performance of space–time adaptive processing (STAP) will be degraded. To solve this problem, a cyclic training sample selection and cancellation (CTSSC) algorithm based on sample matrix inversion (SMI) statistic is proposed. This algorithm consists of three steps. Firstly, a cyclic process is carried out to detect outliers. In each detection cycle, the sample matrix is estimated by all the training samples contained in the training sample set, and the outliers with relatively high jamming-to-noise ratio (JNR) will be detected. Then the corresponding training samples are removed from the training sample set and the sample matrix is updated. This cycle continues until the SMI statistics of all the remaining training samples do not exceed the detection threshold. Secondly, for each outlier detected by the first step, it will be reconstructed by the estimated complex amplitude and steering vector. Then the outlier will be cancelled by the corresponding reconstructed outlier. Finally, conventional STAP will be carried out to suppress clutter. Simulation results validate the effectiveness of the CTSSC algorithm with measured data.