Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint

Hongtao Li,Chaoyu Wang,Xiaohua Zhu,Ke Wang,Yapeng He

doi:10.1155/2015/765385

Abstract

A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.

Highlights

As an important branch of array signal processing, adaptive beamforming technique has achieved a wide range of applications in the fields of radar, sonar, wireless communication, radio astronomy, and so forth [1]
Adaptive beamforming confronts the problem of intense decrease in robustness in the cases that array steering vector (ASV) has errors, or receipt signal contains desired signal component, or the ideal covariance matrix is replaced by signal covariance matrix with finite snapshots [2]
It can be proven that the errors caused by using signal covariance matrix could be treated as the ASV errors at the circumstance of finite snapshots [3]

Summary

Introduction

As an important branch of array signal processing, adaptive beamforming technique has achieved a wide range of applications in the fields of radar, sonar, wireless communication, radio astronomy, and so forth [1]. Magnitude response constraints-based robust adaptive beamforming algorithm is blessed with favorable robustness against ASV errors by forming flat response in main beam while it demands prior information of main beam width and extra interference along with noise can be involved in the range of main beam [14,15,16]. To solve these problems, in this paper, a robust adaptive beamforming algorithm based on the worst-case and norm constraint (RAB-WC-NC) is proposed. The proposed algorithm can improve the robustness of beamforming and suppress interference with finite snapshots

The Signal Model

The Proposed Algorithm

Simulation

Conclusion