The authors derive an online multiple hypothesis Shiryayev sequential probability ratio test (SSPRT) by adopting a dynamic programming approach. It is shown that for a certain criterion of optimality, this generalized Shiryayev SPRT detects and isolates a change in hypothesis in the conditionally independent measurement sequence in minimum time, unlike the Wald SPRT, which assumes the entire measurement sequence to correspond to a single hypothesis. The measurement cost, the cost of a false alarm, and the cost of a miss-alarm are considered in our dynamic programming analysis. The algorithm is shown to be optimal in the infinite time case. Finally, the performance of the algorithm is evaluated by using a few examples. In particular, they implement the algorithm in a fault detection and identification scheme for advanced vehicle control systems.


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    Title :

    A generalized Shiryayev sequential probability ratio test for change detection and isolation


    Contributors:

    Published in:

    Publication date :

    1999


    Size :

    13 Seiten, 10 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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