Spectral classification is a commonly used technique for discriminating between two or more signals. One popular approach to spectral classification utilizes the autoregressive model. In this model a white Gaussian random process is filtered by an all-pole filter. The autoregressive model leads to a classifier derived from the asymptotic Gaussian likelihood function. Despite substantial prior research effort put into developing a robust classifier, the ability of classifiers to discriminate between signals is not great and in some instances is not even satisfactory. A non-homogeneous Poisson process is an alternative way to model the power spectral density. This type of model leads to a different likelihood function, the realizable Poisson likelihood function. Monte Carlo simulations and data analyses demonstrate that the realizable Poisson likelihood function classifier is more robust then the asymptotic Gaussian classifier. The realizable Poisson likelihood function classifier has a greater probability of correct classification than the asymptotic Gaussian for signals with low signal-to-noise ratios, channel distortion, or certain pole locations.
Andrew W. Tucker and Steven Kay, "Robust spectral classification," Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106461F (Presented at SPIE Defense + Security: April 19, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2304616.
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