Robust estimation of sinusoidal signals with colored noise using decentralized processing

A technique is developed for the estimation of the number of signals and their central frequencies using decentralized processing, when it is known a priori that the observations consist of a finite number of sinusoidal signals corrupted by an additive colored random noise process with unknown correlations. Such a noise sequence may be caused by jamming from a hostile agent. The authors´ decentralized processing scheme is one in which each sensor estimates the frequencies and their covariance matrix and sends the results to the fusion center. At the fusion center, since the estimates from the sensors have a mixture density that is possibly not Gaussian, a robust technique is utilized to combine the estimates. Even when the numbers of frequencies transmitted by the various sensors are identical, determining corresponding frequencies from each sensor is not a straightforward task. Also, outliers caused by line splitting or by spurious frequencies are hard to detect. These problems can be resolved by two methods: the so-called refitting method and the ranking method. Algorithms for both are presented in detail