TCDA：用于缺陷L形阵列的鲁棒2D-DOA估计
TCDA: Robust 2D-DOA Estimation for Defective L-Shaped Arrays
AI总结：
TCDA通过将缺陷阵列问题转化为张量空间的数据恢复任务，利用低秩结构和ALS算法实现鲁棒的2D-DOA估计，具有自愈能力和无需额外处理的特性。
摘要：
尽管基于张量的方法在方向到达（DOA）估计中表现出色，但当阵列存在故障或稀疏且违反所需 manifold 结构时，其性能会严重下降。为了解决这一挑战，我们提出了Tensor Completion for Defective Arrays（TCDA），一种鲁棒算法，将物理缺陷问题重新表述为虚拟张量空间中的数据恢复任务。我们详细推导了通过子阵分组、交叉相关和维度重塑从故障阵列信号构建不完整第三阶平行因子分析（PARAFAC）张量的方法。利用张量的内在低秩结构，基于交替最小二乘（ALS）的算法直接从不完整观测中恢复包含DOA参数的因子矩阵。该方法提供了一种软件定义的“自愈”能力，展示了在随机元素故障情况下卓越的鲁棒性，无需额外的DOA估计处理步骤。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
