Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments

Abstract

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in clutter-limited environments. Effective STAP implementation is dependent on accurate space-time covariance matrix estimation. Heterogeneous clutter, including spiky, spatial clutter variation, violates underlying STAP training assumptions and can significantly degrade corresponding detection performance. This paper develops a spiky, space-time clutter model based on the K-distribution, assesses the resulting impact on STAP performance using traditional methods, and then proposes and evaluates the utility of the knowledge-aided parametric covariance matrix estimation (KAPE) method, a model-based scheme that rapidly converges to better represent spatial variation in clutter properties. Via numerical simulation of an airborne radar scenario operating in a spiky clutter environment, we find substantial improvement in probability of detection (P D ) for a fixed probability of false alarm (P FA ) for the KAPE method. For example, in the spiky clutter environment considered herein, results indicate a P D of 32% for traditional STAP and in excess of 90% for KAPE at a P FA of 1E-4, with a corresponding difference of 11.5 dB in threshold observed from exceedance analysis. The proposed K-distributed spiky clutter model, and application and assessment of KAPE as an ameliorating STAP technique, contribute to an improved understanding of radar detection in complex clutter environments.

Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar

“…In (10), k is the normalization constant. The estimation accuracy of the covariance matrix is critical to optimum STAP performance.…”

“…Interference in a particular range bin is characterized by averaging the covariance matrices from the surrounding range bins [10]. The accuracy of the covariance matrix for the particular range bin is strongly reliant on the availability of homogeneous training cells.…”

Radar and Radio Signal Processing

“…In the area of parameter estimation, Bang et al examined knowledge-aided covariance matrix estimation in complex clutter environments [6].…”

“…Two papers were selected as feature papers for the special issue: "Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm" by Egger et al [3] and "Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments" by Bang et al [6].…”

“…This special issue [2] has eight papers covering a diversity of categories in radar signal processing, including three papers on radar optimization and system design [3][4][5], one paper on parameter estimation [6], three papers in the area of synthetic aperture radar (SAR) and inverse SAR (ISAR) processing [7][8][9], and one paper on harmonic radar [10].…”