Nonuniform Sampling for Detection of Abrupt Changes

In this work, detection of abrupt changes in continuous-time linear stochasticsystems and selection of the sampling interval to improve the detection performance areconsidered. Cost functions are proposed to optimize both uniform and nonuniform samplingintervals for the well-known cumulative sum algorithm. Some iterative techniquesare presented to make online optimization computationally feasible. It is shown that considerableimprovement in the detection performance can be obtained by using nonuniformsampling intervals.