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, the author develops 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, he obtains 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 E-contamination and Kolmogorov classes. These estimates are robust against variations in the amplitude distribution of the noise and take into account sensor placement. Given these estimates, existing array processing algorithms designed for Gaussian circumstances can be used on non-Gaussian problems. He also demonstrates a parametric structured estimate of the spatial correlation matrix which allows estimation of the arrival angles directly. A method of exactly determining the class of p.d.f.s is developed which only depends on the time domain noise amplitude distributions being second order processes. Knowledge of this p.d.f. class allows development of algorithms which can be used in the presence of any type of second-order noise process and which perform nearly as well as existing ones do with Gaussian noise.
- Research Organization:
- Rice Univ., Houston, TX (USA)
- OSTI ID:
- 6038475
- Country of Publication:
- United States
- Language:
- English
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