State Space System Identification Approach to Radar Data Processing

Abstract

Space-time adaptive processing (STAP) algorithms typically consist of a data transformation step to reduce the number of degrees of freedom and a sampling step wherein radar returns from adjacent range bins are used to estimate interference statistics. The reduction in the number of degrees of freedom, inadequate sample support, presence of target in sampled data, and range dependence of interference are some of the main reasons for STAP performance loss. In this paper, we present an approach to target detection and localization that mitigates these performance losses using the well-known stochastic realization algorithm from system identification theory. We first identify a state space model from the radar return data in range-pulse domain for a given range bin, and then perform detection and localization using the identified state space matrices. As interference statistics are not directly computed and since there is no sampling from adjacent range bins, this approach is more robust to sample support issues, target in training and range dependence of clutter. A numerical comparison of the approach with beam-space post-Doppler STAP using simulated data is given.