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Covariance Matrix Construction via Preprocessing-Based Spatial Sampling for Adaptive Beamforming
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DOI：10.1109/TAES.2025.3615573.png)
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Abstract
En 中文
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 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 matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by the sample covariance matrix 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.
Keywords:
Covariance matrix reconstruction
direction-of-arrival (DoA)
robust adaptive beamforming (RAB)
spatial spectrum process
Journal
IF:
5.7
Papers:
686
Citations:
2.4W