Abstract
We assume that the noise which interferes with signal detection can be considered to be a mixture of non-stationary, high-amplitude, non-Gaussian components plus a low amplitude Gaussian stationary component. Such a model appears to be widely applicable. The methodology that we propose for signal detection is to identify, categorize, model, and remove the non-Gaussian components in a piece-wise fashion based on their ease of separability from the background Gaussian noise and weak signals. This approach to modeling and processing complicated and non-stationary data is similar to that of experimental physicists going back at least to the time of Newton and perhaps most clearly articulated by Eugene Wigner in his Nobel Prize lecture [1]. Liu and Nolte [2] and Claus, Kadota, and Romain [3] have shown that when the noise is Gaussian and consists of a sum of a strong highly coherent component and a weak component of independent noise samples, then estimation and subtraction of the coherent noise component is nearly optimal. The application of special data smoothers and data cleaners by Martin and Thomson [4] for obtaining robust spectral estimates when the data is contaminated by outliers has provided another motivation for our use of adaptive differential quantization for robust detection.
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Tufts, D.W., Kirsteins, I.P., Swaszek, P.F., Efron, A.J., Melissinos, C.D. (1989). Detection of Signals in the Presence of Strong, Signal-Like Interference and Impulse Noise. In: Wegman, E.J., Schwartz, S.C., Thomas, J.B. (eds) Topics in Non-Gaussian Signal Processing. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8859-3_12
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DOI: https://doi.org/10.1007/978-1-4613-8859-3_12
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