Antenna array calibration in wireless communications

Imperial Users onl

Estimation of DOAs of Acoustic Sources in the Presence of Sensors with Uncertainties

Direction of Arrival (DOA) estimation finds its practical importance in sophisticated video conferencing by audio visual means, locating underwater bodies, removing unwanted interferences from desired signals etc. Some efficient algorithms for DOA estimation are already developed by the researchers . The performance of these algorithms is limited by the fact that the receiving antenna array is affected by some uncertainties like mutual coupling, antenna gain and phase error etc. So considerable attention is there in recent research on this area. In this research work the effect of mutual coupling and the effect of antenna gain and phase error in uniform linear array (ULA) on the direction finding of acoustic sources is studied. Also this effect for different source spacing is compared. For that, estimates of the directions of arrival of all uncorrelated acoustic signals in the presence of unknown mutual coupling has been found using conventional Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). Also DOAs are computed after knowing the coupling coefficients so that we can compare the two results. Simulation results have shown the fact that the degradation in performance of the algorithm due to mutual coupling becomes more if the sources become closer to each other. Also we have estimated DOAs in the presence of unknown sensor gain and phase errors and we have compared this results with the results we got by considering ideal array. Finally in this case also the effect of gain and phase error as the source spacing varies has been tested. Simulation results verify that performance degradation is more if the sources become closer

Minimum Sensitivity Based Robust Beamforming with Eigenspace Decomposition

An enhanced eigenspace-based beamformer (ESB) derived using the minimum sensitivity criterion is proposed with significantly improved robustness against steering vector errors. The sensitivity function is defined as the squared norm of the appropriately scaled weight vector and since the sensitivity function of an array to perturbations becomes very large in the presence of steering vector errors, it can be used to find the best projection for the ESB, irrespective of the distribution of additive noises. As demonstrated by simulation results, the proposed method has a better performance than the classic ESBs and the previously proposed uncertainty set based approach