DOA estimation in a distributed optimization framework: a sparse approach based on consensus ADMM implementation

Traditional direction-of-arrival (DOA) estimation methods use a single processor to deal with the array data. In recent years, the increasing of the scale of sensor arrays brings heavy workload for single processor. Distributed optimization based on multiple local processors has become one of the current research hotspots due to the advantage of parallel computing. In this paper, we proposed a distributed DOA estimation method for massive large-scale arrays. First of all, we provide the signal model and the distributed optimization problem based on sparse representation in a distributed framework. Then, the optimization problem is solved by the alternating direction multiplier method (ADMM), where the overall structure of array is not changed. Compared with the centralized method, our distributed method can greatly reduce the computational complexity while ensuring the estimation accuracy under the large aperture array. Simulation results are provided to show the superiorities of our method.