Robust Matched Field Processing Using an Empirical Characteristic Function Approach Under Impulsive Noise Environments

Matched Field Processing (MFP) is an inversion technique often employed in source localization applications. Conventional MFP approaches are incapable of producing precise results in the presence of extremely impulsive noises, which are typically present in actual applications such as underwater acoustics. This is because the covariance matrix for this category of noises does not converge. Moreover, impulsive noise suppression algorithms fail to provide accurate results. Particularly, fractional lower order moment (FLOM)-based approaches have an unbounded output, and data trimming methods introduce uncertainty into the estimation covariance matrix. In this study, a novel MFP method employing the empirical characteristic function (ECF) is developed. The desirable properties of the characteristic function (CF) result in a robust localization method that is ideally suited for extremely strong tailed noise environments. Using the CF array output, a new covariance-like matrix that can be used in MFP methods has been constructed. To demonstrate the efficiency of the ECF-MFP technique, experiments are conducted in a water tank. Experimental results reveal that this method is very robust in the presence of very heavy tailed noise, a low signal-to-noise ratio, and a tiny sample size. Additionally, it outperforms previous approaches in terms of resolution probability.