Allerton 2015 Paper Abstract


Paper ThA2.4

Cao, Yang (Georgia Institute of Technology), Xie, Yao (Georgia Institute of Technology)

Multi-Sensor Gradual Change Detection

Scheduled for presentation during the Invited Session "Sequential and Quickest Change Detection" (ThA2), Thursday, October 1, 2015, 09:30−09:50, Solarium

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Detection and Estimation, Sensor Networks, Statistical Signal Processing


We develop a mixture procedure to monitor parallel streams of data for a change-point that causes gradual change of a subset of data streams. We model the gradual change as a change in the trends of the affected data streams. Observations are assumed initially to be independent standard normal random variables with zero mean. After a change-point the observations in a subset of the streams of data have mean values that increase or decrease with time. The rate of change for the affected sensors may be different for the affected sensors. The subset and the post-change means are unknown but we assume the number of affected sensors is small. Our procedure uses a mixture statistics which hypothesizes an assumed fraction p0 of affected data streams. An analytic expression is obtained for the average run length (ARL) when there is no change and is shown by simulations to be very accurate. Similarly, an approximation for the expected detection delay (EDD) after a change-point is also obtained. Numerical examples based on real-data demonstrate the good performance of the proposed procedure on real data.



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