Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP

Authors

Guohao Sun, University of Electronic Science and Technology of China
Zishu He, University of Electronic Science and Technology of China
Jun Tong, University of WollongongFollow
Xuejing Zhang, University of Electronic Science and Technology of China

RIS ID

121651

Publication Details

G. Sun, Z. He, J. Tong & X. Zhang, "Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP," IEEE Geoscience and Remote Sensing Letters, vol. 15, (4) pp. 527-531, 2018.

Abstract

This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space-time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP.