Trade-offs among signal correlation, SNR, and number of snapshots for direction of arrival estimation using sparse arrays

Abstract

Arrays can be designed as a full array of sensors or a sparse array of sensors with an equivalent aperture. Reductions in cost, size, weight and power consumption are important factors to consider when designing an array of sensors. Sparse arrays can provide a reduction in these factors. It is important to observe the performance of sparse arrays in relation to full arrays to see if they are able to achieve similar performance. The ability for the full and sparse arrays of sensors to accurately estimate the direction of arrival (DOA) of plane waves impinging them is observed in this thesis. The Mean-squared error (MSE) of the DOA estimation is measured against the Creamer-Rao bound (CRB) as a benchmark for optimal performance for an unbiased estimator. We derive an optimal way to remove sensors for one signal impinging on an array by finding the minimum CRB. This method considers all the unique combinations a sparse array could be assembled for a given number of sensors and aperture.