Adaptive low rank beamforming

Ernesto L dos Santos, Purdue University

Abstract

Under conditions of low sample support, a low-rank solution of the Minimum Variance Distortionless Response (MVDR) equations can yield a higher output SINR than the full-rank MVDR beamformer. In this thesis, several low-rank beamforming techniques are investigated and a new beamformer referred to as the Indirect Dominant Mode Rejection (IDMR) is proposed. We analyze the degradation in the output SINR caused by residual cross-correlations embedded in the sample covariance matrix due to low sample support. The IDMR beamformer is based on a parametric estimate of the covariance matrix, in which any cross-correlation is canceled out. Simulations analysis reveal that the IDMR beamformer yields a dramatic improvement in output SINR relative to the Conjugate Gradients (CG), Principle Component Inverse (PCI) and Dominant Mode Rejection (DMR) beamformers. The performance of the IDMR beamformer can be seriously compromised by array manifold mismatch, also known as steering vector mismatch. In this work we develop and implement algorithms to estimate direction-independent steering vector mismatch towards enabling the IDMR beamformer to operate under this condition. In the investigation of the low-rank CG beamformer, we address the issue of whether the unity gain constraint in the look-direction should be enforced a priori via the use of a blocking matrix or effected a posteriori through simple scaling of the beamforming vector. Remarkably, it is proven that the two methods yield exactly the same low-rank beamformer at each and every rank. ^

Degree

Ph.D.

Advisors

Major Professor: Michael D. Zoltowski, Purdue University.

Subject Area

Engineering, Electronics and Electrical

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