Mitigating the effects of array uncertainties on the performance of the MUSIC algorithm

A novel robust technique is proposed to mitigate the degrading effects of finite sampling and imprecise modeling on the performance of the MUSIC (multiple signal classification) algorithm. Firstly, a new array-signal state-space model is developed for a general array geometry. Based on this model, formulated in a mixed H²/H^∞ framework, a new robust direction-finding technique is proposed. Finally, this theoretical framework is supported by simulation examples which demonstrate the robustness of the new technique to the presence of array uncertainties and signal correlation