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Generalized maximum correntropy detector for non‐Gaussian environments

This paper addresses the problem of multiple‐hypothesis detection. In many applications, assuming the Gaussian distribution for undesirable disturbances does not yield a sufficient model. On the other hand, under the non‐Gaussian noise/interference assumption, the optimal detector will be impractically complex. Therewith, inspired by the optimal maximum likelihood detector, a suboptimal detector is designed. In particular, a novel detector based on the generalized correntropy, which adopts the generalized Gaussian density function as the kernel, is proposed. Simulations demonstrate that, in non‐Gaussian noise models, the generalized correntropy detector significantly outperforms other commonly used detectors. The efficient and robust performance of the proposed detection method is illustrated in both light‐tailed and heavy‐tailed noise distributions.