Sparsity-based Distributed DoA Estimation for Radar Networks
Batu Krishna Chalise, Daniel M. Wong, Moeness G. Amin, R. Michael Buehrer, Anthony F. Martone, Benjamin H. Kirk, Kelly D. Sherbondy · 2022 IEEE Radar Conference (RadarConf22) · 2022
In this paper distributed estimation of direction of arrival (DoA) is proposed for a network of radar nodes, in which nodes share their limited information related to their decisions with only their neighboring nodes. The sparsity of target scenario is exploited and distributed DoA estimation is formulated as the estimation of sparse vectors. The neighboring nodes aim to achieve a consensus on their estimation of these sparse vectors. The estimation problem is solved iteratively with alternating direction of method of multipliers (ADMM) method. Each node leverages co-prime array configuration, a type of structured sparse array, to enable direction finding for more sources than the number of node antennas. Numerical simulations show that the proposed distributed method converges within few iterations and provides much improved spatial spectra than local (non-distributed) estimations.