TCDA: Robust 2D-DOA Estimation for Defective L-Shaped Arrays

2026-02-24

AI Summary

This work proposes a robust two-dimensional direction-of-arrival (2D-DOA) estimation method based on tensor “self-healing” to address the severe performance degradation of conventional algorithms when array failures or sparsity disrupt the underlying manifold structure. By partitioning the array into subarrays, performing cross-correlation operations, and reshaping dimensions, the method constructs a third-order PARAFAC tensor with missing entries. Leveraging the tensor’s low-rank property, it directly recovers the factor matrices containing DOA information—without requiring additional calibration or explicit data imputation. The approach achieves high-accuracy 2D-DOA estimation even under random sensor failures, significantly enhancing robustness and eliminating dependence on full array integrity. Experimental results on L-shaped arrays demonstrate its superiority over existing techniques.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

While tensor-based methods excel at Direction-of-Arrival (DOA) estimation, their performance degrades severely with faulty or sparse arrays that violate the required manifold structure. To address this challenge, we propose Tensor Completion for Defective Arrays (TCDA), a robust algorithm that reformulates the physical imperfection problem as a data recovery task within a virtual tensor space. We present a detailed derivation for constructing an incomplete third-order Parallel Factor Analysis (PARAFAC) tensor from the faulty array signals via subarray partitioning, cross-correlation, and dimensional reshaping. Leveraging the tensor's inherent low-rank structure, an Alternating Least Squares (ALS)-based algorithm directly recovers the factor matrices embedding the DOA parameters from the incomplete observations. This approach provides a software-defined'self-healing'capability, demonstrating exceptional robustness against random element failures without requiring additional processing steps for DOA estimation.

Problem

• 2D-DOA estimation
• defective arrays
• tensor completion
• L-shaped arrays
• manifold structure

Innovation

• Tensor Completion
• Defective Array
• 2D-DOA Estimation
• PARAFAC
• Self-healing