用于联合DOA估计与失真传感器检测的低秩行稀疏分解
Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection
AI总结：
针对增益相位误差导致的失真传感器问题，提出低秩行稀疏分解框架及IRLS算法，实现联合DOA估计与失真传感器检测，并在收敛、成本和估计性能上优于多种先进方法。
摘要：
失真传感器可能随机出现，并可能导致传感器阵列系统崩溃。我们考虑一种阵列模型，其中少量传感器受到未知传感器增益和相位误差的失真影响。在这种阵列模型下，研究了联合波达方向（DOA）估计与失真传感器检测问题，并将该问题构建在低秩与行稀疏分解框架之下。我们推导了一种迭代重加权最小二乘（IRLS）算法，用于在无噪声和有噪声两种情形下求解所得问题。通过目标函数的单调性和有界性，分析了IRLS算法的收敛性质。围绕参数选择、收敛速度、计算复杂度以及DOA估计和失真传感器检测性能，开展了大量仿真。尽管IRLS算法在检测失真传感器方面略逊于交替方向乘子法，但结果表明，我们的方法在收敛速度、计算成本和DOA估计性能方面优于若干现有先进技术。
英文摘要：
Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. We consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is investigated and the problem is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem in both noiseless and noisy cases. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating direction method of multipliers in detecting the distorted sensors, the results show that our approach outperforms several state-of-the-art techniques in terms of convergence speed, computational cost, and DOA estimation performance.
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