Robust nonhomogeneous training samples detection method for space–time adaptive processing radar using sparse-recovery with knowledge-aided

Zhihui Li; Hanwei Liu; Yongshun Zhang; Yiduo Guo

doi:10.1117/1.JRS.11.045013

8 December 2017 Robust nonhomogeneous training samples detection method for space–time adaptive processing radar using sparse-recovery with knowledge-aided

Zhihui Li, Hanwei Liu, Yongshun Zhang, Yiduo Guo

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 045013 (December 2017). https://doi.org/10.1117/1.JRS.11.045013

Abstract

The performance of space–time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial–temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial–temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

Citation Download Citation

Zhihui Li, Hanwei Liu, Yongshun Zhang, and Yiduo Guo "Robust nonhomogeneous training samples detection method for space–time adaptive processing radar using sparse-recovery with knowledge-aided," Journal of Applied Remote Sensing 11(4), 045013 (8 December 2017). https://doi.org/10.1117/1.JRS.11.045013

Received: 22 August 2017; Accepted: 22 November 2017; Published: 8 December 2017

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