Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. We consider an model within which small number are distorted by unknown gain phase errors. With such model, problem joint direction-of-arrival (DOA) estimation detection is formulated under framework low-rank row-sparse decomposition. derive iteratively reweighted least squares (IRLS) algorithm solve resulting problem. The convergence property IRLS analyzed means monotonicity boundedness objective function. Extensive simulations conducted regarding parameter selection, speed, computational complexity, performances DOA as well detection. Even though slightly worse than alternating direction method multipliers in detecting sensors, results show that our approach outperforms several state-of-the-art techniques terms cost, performance.