Multiple mainlobe interferences suppression based on subspace matrix filtering and covariance matrix reconstruction

Yasen Wang; Qinglong Bao; Zengping Chen

doi:10.1117/1.JRS.10.035008

1 August 2016 Multiple mainlobe interferences suppression based on subspace matrix filtering and covariance matrix reconstruction

Yasen Wang, Qinglong Bao, Zengping Chen

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 035008 (August 2016). https://doi.org/10.1117/1.JRS.10.035008

Abstract

In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first. Then, the eigenvectors associated with mainlobe interference are determined and the eigen-projection matrix can be calculated. Next, the sidelobe-interference-plus-noise covariance matrix is reconstructed through eigenvalue replacement procedure. Finally, we can get the adaptive weight vector. Simulation results demonstrate the effectiveness of the proposed method when multiple mainlobe interferences exist.

Citation Download Citation

Yasen Wang, Qinglong Bao, and Zengping Chen "Multiple mainlobe interferences suppression based on subspace matrix filtering and covariance matrix reconstruction," Journal of Applied Remote Sensing 10(3), 035008 (1 August 2016). https://doi.org/10.1117/1.JRS.10.035008

Published: 1 August 2016

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