Change detection with unknown post-change parameter using Kiefer-Wolfowitz method

We consider a change detection problem with an unknown post-change parameter. The optimal algorithm in minimizing worst case detection delay subject to a constraint on average run length, referred as parallel CUSUM, is computationally expensive. We propose a low complexity algorithm based on paramet...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 3919 - 3923
Hlavní autoři: Singamasetty, Vijay, Nair, Navneeth, Bhashyam, Srikrishna, Pachai Kannu, Arun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2017
Témata:
average run length
Change detection algorithms
Convergence
CUSUM
Delays
Detection delay
false alarm probability
Kiefer-Wolfowitz algorithm
Mathematical model
Tuning
ISSN:2379-190X
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Shrnutí:We consider a change detection problem with an unknown post-change parameter. The optimal algorithm in minimizing worst case detection delay subject to a constraint on average run length, referred as parallel CUSUM, is computationally expensive. We propose a low complexity algorithm based on parameter estimation using Kiefer-Wolfowitz (KW) method with CUSUM based change detection. We also consider a variant of KW method where the tuning sequences of KW method are reset periodically. We study the performance under the Gaussian mean change model. Our results show that reset KW-CUSUM performs close to the parallel CUSUM in terms of worst case delay versus average run length. Non-reset KW-CUSUM algorithm has smaller probability of false alarm compared to the existing algorithms, when run over a finite duration.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7952891