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

arXiv: https://arxiv.org/abs/2510.17823 · HTML: https://mlsanity.com/paper/2510.17823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 92 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents](https://mlsanity.com/paper/1812.09985/) (2018)
- [URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming](https://mlsanity.com/paper/2210.02214/) (2022)

## Abstract (arXiv)

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.