Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming

Saeed Mohammadzadeh, Rodrigo C. de Lamare, Yuriy Zakharov

TL;DR

This paper tackles robust adaptive beamforming under steering-vector mismatches and covariance reconstruction challenges. It introduces preprocessing-based spatial sampling (PPBSS) to build an interference-plus-noise covariance approximation from adaptive angular sectors, enabling a generalized linear combination (GLC) shrinkage with the identity matrix and a sector-based interference model. A power-method-based approach then estimates the dominant SOI covariance eigenpair to obtain the steering vector, while the IPNC matrix is reconstructed with reduced complexity compared to full eigendecomposition. Simulations show that PPBSS achieves higher SINR under various mismatch conditions and array perturbations, with lower computational cost than competing methods, demonstrating practical robustness for real-time beamforming.

Abstract

This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the interfering signals are computed adaptively. Then, we utilize the well-known general linear combination algorithm to reconstruct the interference-plus-noise covariance (IPNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by the sample covariance matrix (SCM) in the shrinkage method. A power spectrum sampling strategy is then devised based on a preprocessing matrix computed with the estimated angular sectors' information. Moreover, the covariance matrix for the signal is formed for the angular sector of the signal-of-interest (SOI), which allows for calculating an SV for the SOI using the power method. An analysis of the array beampattern in the proposed PPBSS technique is carried out, and a study of the computational cost of competing approaches is conducted. Simulation results show the proposed method's effectiveness compared to existing approaches.

Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming

TL;DR

This paper tackles robust adaptive beamforming under steering-vector mismatches and covariance reconstruction challenges. It introduces preprocessing-based spatial sampling (PPBSS) to build an interference-plus-noise covariance approximation from adaptive angular sectors, enabling a generalized linear combination (GLC) shrinkage with the identity matrix and a sector-based interference model. A power-method-based approach then estimates the dominant SOI covariance eigenpair to obtain the steering vector, while the IPNC matrix is reconstructed with reduced complexity compared to full eigendecomposition. Simulations show that PPBSS achieves higher SINR under various mismatch conditions and array perturbations, with lower computational cost than competing methods, demonstrating practical robustness for real-time beamforming.

Abstract

This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the interfering signals are computed adaptively. Then, we utilize the well-known general linear combination algorithm to reconstruct the interference-plus-noise covariance (IPNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by the sample covariance matrix (SCM) in the shrinkage method. A power spectrum sampling strategy is then devised based on a preprocessing matrix computed with the estimated angular sectors' information. Moreover, the covariance matrix for the signal is formed for the angular sector of the signal-of-interest (SOI), which allows for calculating an SV for the SOI using the power method. An analysis of the array beampattern in the proposed PPBSS technique is carried out, and a study of the computational cost of competing approaches is conducted. Simulation results show the proposed method's effectiveness compared to existing approaches.
Paper Structure (18 sections, 61 equations, 13 figures, 1 algorithm)

This paper contains 18 sections, 61 equations, 13 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Prior and Related Works
  3. Contribution
  4. Signal Model and Problem Background
  5. The Proposed PPBSS-IPNC Algorithm
  6. Interference DoAs and pre-processed matrix estimation
  7. IPNC Matrix Reconstruction
  8. Desired signal steering vector estimation
  9. Analysis of PPBSS
  10. Performance Analysis of DoA Estimation Using CRB
  11. Computational Complexity
  12. The Array Beampattern of the Proposed PPBSS
  13. Simulation Results
  14. Mismatch due to look direction error
  15. Random signal look direction mismatch
  16. ...and 3 more sections

Figures (13)

  • Figure 1: Correlation of theoretical IPNC \ref{['theoretical IPNC']} versus proposed IPNC \ref{['tilde Ri+n']} for SNR =-20 dB,
  • Figure 2: Eigenvalue calculation of the power method versus the iterations
  • Figure 3: Performance evaluation of the proposed PPBSS method under the interference DoA estimator.
  • Figure 4: The beampattern for different values of $\rho$, DoA of desired signal=$0^o$ and the DoA of interferences= $30^o, 50^o$
  • Figure 5: The beampattern for different values of $\eta$, DoA of desired signal=$0^o$ and the DoA of interferences= $30^o, 50^o$
  • ...and 8 more figures