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

• Main Authors: Fangzhou Wang, Pu Wang, Xin Zhang, Hongbin Li, Braham Himed
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Parametric modeling; adaptive target detection; phased-array radar; distributed multi-input multi-output (MIMO) radar; passive radar
• Online Access: https://ieeexplore.ieee.org/document/9406810/

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