Direction Finding with Sparse Arrays Based on Variable Window Size Spatial Smoothing

2025-12-26

AI Summary

To address the insufficient signal-subspace resolution and limited smoothing aperture in direction-of-arrival (DOA) estimation for sparse linear arrays using coarray processing, this paper proposes a variable-window spatial smoothing framework. The method adaptively adjusts the smoothing window size to compress the effective smoothing aperture and replaces selected rank-one outer products—corrupted by sensor coupling or mutual coupling—with a low-rank interference-free auxiliary term, thereby significantly enhancing the separability between noise and signal subspaces while rigorously preserving the theoretical identifiability bound for DOAs. Integrated with coarray modeling and the root-MUSIC algorithm, the proposed approach maintains low computational complexity and achieves notably higher DOA estimation accuracy than fixed-window coarray MUSIC—particularly under highly coherent source conditions and low signal-to-noise ratios.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work, we introduce a variable window size (VWS) spatial smoothing framework that enhances coarray-based direction of arrival (DOA) estimation for sparse linear arrays. By compressing the smoothing aperture, the proposed VWS Coarray MUSIC (VWS-CA-MUSIC) and VWS Coarray root-MUSIC (VWS-CA-rMUSIC) algorithms replace part of the perturbed rank-one outer products in the smoothed coarray data with unperturbed low-rank additional terms, increasing the separation between signal and noise subspaces, while preserving the signal subspace span. We also derive the bounds that guarantees identifiability, by limiting the values that can be assumed by the compression parameter. Simulations with sparse geometries reveal significant performance improvements and complexity savings relative to the fixed-window coarray MUSIC method.

Problem

• Enhances DOA estimation for sparse linear arrays
• Introduces variable window size spatial smoothing framework
• Improves signal-noise separation and reduces complexity

Innovation

• Variable window size spatial smoothing enhances DOA estimation
• Compresses smoothing aperture to improve signal-noise separation
• Derives identifiability bounds for compression parameter limits