Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework

In this paper, we present a new method for Direction of Arrival (DoA) estimation in distributed sensor array networks by using Alternating Direction Method of Multipliers (ADMM) in Sparse Bayesian Learning (SBL) framework. Our proposed method, CDoAE, has certain advantages compared to previous distributed DoA estimation methods. It does not require any special array geometry and there is no need for inter -array frequency and phase matching. CDoAE uses the distributed ADMM to update the parameter set extracted by the SBL frameworks in the local arrays to minimize a common objective function. This update process is implemented in the master-node and the result is distributed back to the slave nodes. It is shown that the performance of the local arrays can be improved significantly in this distributed DOA estimation framework. Moreover, we present a method, CDoAE-TVR, to reduce the number of parameters transmitted from the local arrays to the master array which is important for the networks with limited bandwidth and energy. Several simulations have been performed including the cases with coherent sources. It is shown that the use of ADMM in a distributed fashion improves the SBL output efficiently and effectively. In addition, proposed CDoAE-TVR method reduces the transmitted parameters in the network with a small sacrifice on the DoA estimation performance.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a new method for DoA estimation in distributed sensor array networks using ADMM and SBL framework, showing improved performance in local arrays and reduction in transmitted parameters. Experimental results demonstrate that distributed use of ADMM efficiently enhances DoA estimation performance.