Null broadening adaptive beamforming based on covariance matrix reconstruction and similarity constraint

Junhui Qian,Zishu He,Yile Zhang,Julan Xie

doi:10.1186/s13634-016-0440-1

Abstract

In this paper, a procedure for the null broadening algorithm design with respect to the nonstationary interference is proposed. In contrast to previous works, we first impose nulls toward the regions of the nonstationary interference based on the reconstruction of the interference-plus-noise covariance matrix. Additionally, in order to provide a restriction on the shape of the beam pattern, a similarity constraint is enforced at the design stage. Then, the adaptive weight vector can be computed via maximizing a new signal-to-interference-plus-noise ratio (SINR) criterion subject to similarity constraint. Mathematically, the design original problem is expressed as a nonconvex fractional quadratically constrained quadratic programming (QCQP) problem with additional constraint, which can be converted into a convex optimisation problem by semidefinite programming (SDP) techniques. Finally, an optimal solution can be found by using the Charnes-Cooper transformation and the rank-one matrix decomposition theorem. Several numerical examples are performed to validate the performance of the proposed algorithm.

Highlights

Adaptive beamforming has been one of the most significant research areas in array signal processing, which has been widely used in radar, sonar, wireless communications, and many other fields [1]
We focus on the nonstationary interference suppression problem, namely, the null broadening algorithm with interference-plus-noise covariance matrix reconstruction, and beam pattern similarity constraint is proposed on the condition that the exact knowledge of target parameters is known or previously estimated
We propose a novel null broadening algorithm design with respect to the nonstationary interference, and the design avoids the additional complexity of the weight vector continuously updating

Summary

Introduction

Adaptive beamforming has been one of the most significant research areas in array signal processing, which has been widely used in radar, sonar, wireless communications, and many other fields [1]. The aim of adaptive beamforming algorithm is to extract the desired signal and suppress the interference as well as noise at the array output simultaneously. The conventional beamforming method often suffers severe performance degradation because of some factors, such as small training snapshots and imprecise knowledge in many practical applications [2]. Various adaptive beamformers have been proposed based on different principles to improve the robustness against the desired signal imprecisely [5], such as signal-subspace projection technique, diagonal loading technique, and their variants [6, 7]; all of the methods are quite efficient in. The resulting beamformer weights are frozen and used for the remainder of the frame despite changes in real-world scenario, which considerably degrade the output signal-to-interferenceplus-noise ratio (SINR) performance in nonstationary environments [10]. For large aperture arrays, the perturbation of the interference location represents a serious problem because the directional pattern nulls of them

Objectives

Conclusion