Decentralized cooperative DOA tracking using non-Hermitian generalized eigendecomposition

The problem of direction-of-arrival (DOA) estimation using partly calibrated arrays composed of multiple identically oriented subarrays is considered. The subarrays are assumed to possess the shift-invariance property which is exploited to develop a distributed search-free DOA estimation algorithm that is based on the generalized eigendecomposition (GED) of a pair of covariance matrices. We propose a fully decentralized adaptive algorithm which tracks the generalized eigenvalues (GEVs) of a non-Hermitian pair of covariance matrices, from which the DOAs are estimated. Moreover, to enforce the amplitude property of the nominal source GEDs, we propose a suitable measurement weighting scheme. We demonstrate the estimation performance of our algorithm with simulations and confirm that our algorithm is able to identify more sources than each subarray individually can.