TCDA: Robust 2D-DOA Estimation for Defective L-Shaped Arrays
2026-02-24 • Information Theory
Information Theory
AI summaryⓘ
The authors address a problem where some sensors in an array used to find the direction of signals are broken or missing. They treat this problem like filling in missing puzzle pieces, using a special math tool called tensors to organize the data. By cleverly dividing the sensor data into smaller parts and using an algorithm that guesses the missing information, their method can recover the important direction details even when many sensors fail. This makes the system more reliable without needing extra complicated steps.
Direction-of-Arrival (DOA)tensor completionPARAFAC tensorAlternating Least Squares (ALS)faulty arrayslow-rank structuresubarray partitioningcross-correlationdata recoverysignal processing
Authors
Wenlong Wang, Tianyang Zhang, Tailun Dong, Lei Zhang
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.