Timefrequency maximum likelihood methods for direction finding
This paper proposes a novel timefrequency maximum likelihood (tf ML) method for direction-of-arrival (DOA) estimation for nonstationary signals impinging on a multi-sensor array receiver, and compares this method with conventional maximum likelihood DOA estimation techniques. Timefrequency distributions localize the signal power in the timefrequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different timefrequency signatures permits the division of the timefrequency domain into smaller regions, each containing fewer signals than those incident on the array. The reduction of the number of signals within different timefrequency regions not only reduces the required number of sensors, but also decreases the computational load in multidimensional optimizations. Compared to the recently proposed timefrequency MUSIC (tf MUSIC), the proposed tf ML method can be applied to coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.
maximum likelihood direction finding timefrequency distribution spatial timefrequency distribution array processing