Multiplicity of coprime pairs for extension of coprime sensor arrays

Abstract

This research finds the optimum period of a basic coprime sensor array (CSA) needed to reduce the PSL height to -13 dB for both minimum and product processing methods. Our design does not have the constraint that the subarray lengths be one integer apart unlike in [Adhikari et al., 2014]. We apply MUSIC algorithm to the optimized CSA and compare the PSD estimate obtained by Vaidyanathan and Pal’s covariance matrix estimation using a single sensor pair versus our covariance matrix estimated using all available sensor pairs. We show that minimum processing requires fewer periods than product processing to reach the PSL height of -13 dB. Larger coprime pairs require fewer periods and cause an increase in variance and a decrease in the number of lags available between the two subarrays. This decrease lowers the accuracy of the covariance matrix estimation. However, using all available sensor pairs to generate the covariance matrix greatly increases the accuracy compared to using a single sensor pair. When only one signal is present, both covariance estimates produce a similar, accurate DOA, but as the number of signals increases, our covariance estimate performs better. [Work supported by Louisiana Tech University.]