Robust 2-D spectrum estimation using Radon transform

Abstract

Summary form only given. A robust method of 2-D spectral estimation of signals in additivbe white noise whose distribution is the so-called outlier contaminated Gaussian process was investigated. The term robustness refers here to insensitivity to small deviation in the underlying Gaussian noise assumption. Robust spectral estimation methods are known to be computationally feasible only when the number of parameters to be estimated is small, and recent approaches to 2-D robust spectral estimation require very extensive computation. In the work reported the 2-D spectral estimation problem was converted into a set of 1-D independent problems using the Radon transform. The 2-D array data were transformed into a set of 1-D sequences (projections), and each projection was modeled as a 1-D autoregressive (AR) process. A robust technique based on the Huber's minimax approach was utilized to estimate the AR parameters. The 2-D spectrum was finally obtained on a polar raster. This method is highly amenable to parallel processing.<>