An Overview of Parametric Modeling and Methods for Radar Target Detection With Limited Data

This article provides a survey of recent results on exploiting parametric auto-regressive (AR) models for adaptive radar target detection. Specifically, three types systems are considered, including phased-array with multiple co-located transmitters and receivers, distributed multi-input multi-output (MIMO) widely spatially separated passive which uses existing sources as illuminators opportunity (IOs). These significant interest wide range military civilian applications. For each the radars, we discuss how AR processes can be employed to succinctly model underlying signal correlation efficiently estimate it from limited data, thus enabling effective detection in complex non-homogeneous environments when training data is limited. We illustrate performance such assisted detectors relative conventional non-parametric approaches by using computer simulated experimental data.