Neural Network-Based Compression Framework for DOA Estimation Exploiting Distributed Array
Saidur R. Pavel, Yimin D. Zhang
Abstract
Distributed array consisting of multiple subarrays is attractive for high-resolution direction-of-arrival (DOA) estimation when a large-scale array is infeasible. To achieve effective distributed DOA estimation, it is required to transmit information observed at the subarrays to the fusion center, where DOA estimation is performed. For noncoherent data fusion, the covariance matrices are used for subarray fusion. To address the complexity involved with the large array size, we propose a compression framework consisting of multiple parallel encoders and a classifier. The parallel encoders at the distributed subarrays are trained to compress the respective covariance matrices. The compressed results are sent to the fusion center where the signal DOAs are estimated using a classifier based on the compressed covariance matrices.