Direction finding in spatially correlated noise fields with arbitrarily-spaced and far-separated subarrays at unknown locations

A computationally simple azimuth-elevation direction finding algorithm in spatially correlated noise fields using two-far-separated subarray geometry (Li et al. 1995) is presented. We assume one subarray consists of multiple acoustic vector sensors, while another subarray comprises multiple pressure sensors. All sensors are arbitrarily placed at unknown locations. The authors firstly define a cross matrix to eliminate the effect of the spatially correlated noise. Then the so-called propagator method is used to estimate the steering vectors of acoustic vector sensors. Finally, a closed form, automatically paired azimuth-elevation angle estimates are derived. The proposed algorithm requires no eigen decomposition into signal or noise subspaces. In addition, the proposed algorithm does not need 2D iterative searching. Therefore the algorithm shows low computational complexity. Monte Carlo simulations are presented to verify the effectiveness of the proposed algorithm.