Detection of changes in the spectrum of a multidimensional process

An algorithm is presented for the sequential detection of changes in the spectrum of a multidimensional process. The asymptotic properties of the statistic used are investigated in the case of a real Gaussian process. The algorithm of detection is based on a sequential likelihood-ratio test. Simulations show very good behavior of the algorithm in the case of Gaussian and non-Gaussian processes. In both cases, changes are detected with good accuracy, while the number of false alarms is small.<
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