Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks

2022-8-26
Nurbaş, Ekin
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In recent years, developments in microprocessor and wireless communication technologies have benefited a variety of distributed sensor network applications, including array signal processing. Researchers have been investigating distributed implementations of array signal processing algorithms, such as Direction of Arrival Estimation, for a variety of applications. Performance and practical implementations of those algorithms are affected by a variety of factors, such as inter-array phase and frequency matching and array geometry dependencies. Using the Alternating Direction Method of Multipliers (ADMM) in the Sparse Bayesian Learning (SBL) framework, we provide a new method for Direction of Arrival (DoA) estimation in distributed sensor array networks. Our new method, Collaborative Direction of Arrival Estimation (CDoAE), has a number of advantages over earlier distributed DoA estimate techniques. It does not necessitate any particular array geometry or inter-array frequency and phase matching. To minimize a shared objective function, CDoAE employs the distributed ADMM to update the parameter set extracted by the SBL frameworks in the local arrays. The master-node performs this update procedure, and the result is sent to the slave nodes. It is demonstrated that the performance of local arrays can be greatly enhanced using this distributed DOA estimation method. Furthermore, we provide a method, CDoAE-TVR, for reducing the amount of parameters broadcast from the local arrays to the master array, which is crucial for networks with restricted bandwidth and energy. Several simulations, including scenarios with coherent sources, have been done. It is demonstrated that the widespread use of ADMM improves the effectiveness of the SBL output. In addition, the suggested CDoAE-TVR technique minimizes the transmitted parameters at the cost of a minor reduction in DoA estimation performance.
Direction of Arrival, DoA, Alternating Direction Method of Multipliers, ADMM, Sparse Bayesian Learning, SBL, Distributed Processing, Distributed DoA Estimation
https://hdl.handle.net/11511/98768
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
E. Nurbaş, “Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks,” M.S. - Master of Science, Middle East Technical University, 2022.
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