Registration-based Compensation using Sparse Representation in Conformal-array STAP

TL;DR

This paper introduces a novel registration-based compensation method using sparse representation for conformal-array STAP, significantly improving clutter spectral estimation and detection performance in range-dependent environments.

Contribution

It proposes SR-RBC, a new sparse representation-based spectral estimation technique that enhances accuracy over traditional methods in conformal-array STAP systems.

Findings

More accurate clutter spectral estimation achieved.

Transformed training data exhibit increased stationarity.

Enhanced signal-clutter-ratio (SCR) performance.

Abstract

Space-time adaptive processing (STAP) is a well-known technique in detecting slow-moving targets in the presence of a clutter-spreading environment. When considering the STAP system deployed with conformal radar array (CFA), the training data are range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) is implemented based on a sub-snapshot spectrum using temporal smoothing. In this case, the estimation accuracy of the configuration parameters and the clutter power distribution is limited. In this paper, the technique of sparse representation is introduced into the spectral estimation, and a new compensation method is proposed, namely RBC with sparse representation (SR-RBC). This method first converts the clutter spectral estimation into an ill-posed problem with the constraint of sparsity. Then, the technique of sparse representation, like iterative reweighted least squares (IRLS), is utilized to solve this problem. Then, the transform matrix is designed so that the processed training data behaves nearly stationary with the test cell. Because the configuration parameters and the clutter spectral response are obtained with full-snapshot using sparse representation, SR-RBC provides more accurate clutter spectral estimation, and the transformed training data are more stationary so that better signal-clutter-ratio (SCR) improvement is expected.