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

TL;DR

This paper introduces TCDA, a tensor completion method that robustly estimates 2D-DOA in defective L-shaped arrays by recovering missing data through low-rank tensor factorization, enabling accurate DOA estimation despite array faults.

Contribution

The paper presents a novel tensor completion algorithm for defective arrays that enhances DOA estimation robustness without additional processing.

Findings

Achieves accurate DOA estimation with faulty array elements.

Demonstrates robustness against random element failures.

Provides a self-healing capability for array signal processing.

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.