CRAMER-RAO BOUND ANALYSIS OF DISTRIBUTED DOA ESTIMATION EXPLOITING MIXED-PRECISION COVARIANCE MATRIX

In this paper, we analyze the Cramer-Rao bound of the distributed direction-of-arrival (DOA) estimation problem where the covariance matrix is formulated in a mixed-precision manner. In this scheme, the self-covariance matrix of a subarray is locally computed using the full-precision data received at the subarray, whereas one-bit data are exploited at the fusion center to compute the cross-covariance matrices between different subarrays. As such, the resulting covariance matrix of the distributed array consists of full-precision subarray self-covariance matrices and low-precision cross-covariance matrices between subarrays, thus termed as a mixed-precision covariance matrix. Such distributed DOA estimation scheme offers substantial reduction of the network communication overhead while maintaining the degrees of freedom offered by the distributed array. We provide the Cramer-Rao bound analysis which enables us to understand the importance of the self- and cross-covariance matrices and optimize the array parameters. The CRB analysis results are compared with the root mean-square error performance of the estimated signal DOAs.