Abstract
DOA estimation in the presence of impulsive signals and noises is a challenging issue. Conventional and fractional lower order based methods do not work properly in the presence of very heavy-tailed noise. Interesting properties of empirical characteristic function (ECF) such as great simplicity and high accuracy leads to develop new methods, which are presented in this paper. It is analytically shown that by using the characteristic function of the array signal, a new sample covariance matrix can be extracted to use in algorithms like MUSIC. Numerical methods and the optimum set of parameters are also presented to estimate this matrix from the ECF of the array signal. Monte-Carlo simulation results are also presented which demonstrate the effectiveness and improved performance of the algorithms in highly impulsive environments. It is particularly effective in comparison to available methods in terms of probability of resolution and estimation error. The results indicate that the proposed method has a superior performance under the condition of heavy-tailed noise.
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