Study On Robust Beamforming And Its Applications

Robust detection, localization and beamforming of spatial sources under non-ideal conditions are studied in this thesis and the applications of methods for the wireless communication and radar signal processing. The details of this thesis are organized as follows.1. We present three novel approaches to complete the Robust Beamforming. Firstly, we propose a novel approach to adaptive beamforming that is robust to array errors. The proposed method involves two steps: the first step is to estimate the actual steering vector of the desired signal, and the second is to obtain optimal weight by utilizing the estimated steering vector. Our method belongs to the class of diagonal loading, but the optimal amount of diagonal loading level can be precisely calculated based on the uncertainty set of the steering vector. Moreover, the proposed robust Capon beamforming has an explicit closed-form solution. Secondly, since the first method still belongs to the class of diagonal loading, we changed the constrained term in the optimization function and got one approach that do not belong to the class of the diagonal loading. Finally, the first two methods require eigen-decomposition that needs lots of computations and the number of signals impinging on the array. To overcome this drawback, we use the power of R (POR) technique that does not need eigen-decomposition to obtain the noise subspace.2. Since the performance of cyclostationarity-based array processing methods suffers from severe degradation if there is a small mismatch in the cycle frequency of the signals of interest. The problem of robust adaptive array beamforming for cyclostationary signals is also emphasized. We propose the efficient robust algorithm based on the conventional cyclic adaptive beamformig algorithm against the mismatch in the cycle frequency. The performance of the proposed techniques is compared with that of existing methods via simulation examples, which demonstrate a marked improvement in the robustness against imprecisely knowledge of the cycle frequency. Also, the similar idea is applied to the radar signal processing based on the Doppler signals.