Robust methods tailored for non-Gaussian narrowband array processing
Array processing algorithms generally assume that the received signal, composed of both narrowband signals and noise, is Gaussian, which is not true in general. In the context of the narrowband array processing problem, we develop robust methods to accurately estimate the spatial correlation matrix which also utilize a priori information about the matrix structure. For Gaussian processes, structured estimates have been developed which find the maximum likelihood covariance matrix estimate subject to structural constraints on the covariance matrix (8). However, further problems arise when the noise is non-Gaussian and the estimators for Gaussian processes may lead to grossly inaccurate estimates (17). By minimizing the worst asymptotic estimate variance, we obtain the robust structured maximum likelihood type estimates(M-estimates) of the spatial correlation matrix in the presence of noises with probability density functions (p.d.f.) in the
Williams, Douglas Bennett. "Robust methods tailored for non-Gaussian narrowband array processing." (1989) Diss., Rice University. https://hdl.handle.net/1911/16311.