Performance analysis of time frequency subspace based direction finding algorithms in presence of perturbed array manifold

摘要

Conventional subspace based direction finding approaches such as MUSIC and ESPRIT algorithms commonly use the array data covariance matrix. In non stationary context, the use of the Spatial Time-Frequency Distribution (STFD) instead of the covariance matrix can significantly improve the performance of such algorithms. In this paper we are interested in the performance analysis of such approaches in the presence of both additive noise and perturbed array manifold. An unified expression of the Direction Of Arrival (DOA) error estimation is derived for both approaches. The obtained results show that for low Signal to Noise Ratio (SNR) and high Signal to Sensor Perturbation Ratio (SPR) the STFD based DOA estimations perform better, while for high SNR and for the same SPR both Covariance and STFD based approaches have similar performance.