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

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 3919 - 3923
• Main Authors: Singamasetty, Vijay, Nair, Navneeth, Bhashyam, Srikrishna, Pachai Kannu, Arun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2017
• Subjects: average run length; Change detection algorithms; Convergence; CUSUM; Delays; Detection delay; false alarm probability; Kiefer-Wolfowitz algorithm; Mathematical model; Tuning
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952891

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.