Bayesian multiple change-point detection of propagating events

Topi Halme, Eyal Nitzan, Visa Koivunen

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Detection of multiple spatial events in parallel is of wide interest in many modern applications, such as Internet of Things, environmental monitoring, and wireless communication. Sensor networks can be used for acquiring data and performing inference. In this paper, we take a Bayesian approach and model the detection of spatial events as a Bayesian multiple change point detection problem. The sensor network is assumed to be divided into distinct known clusters. In each cluster, a point source generates a spatial event that propagates omnidirectionally. The event causes a change in the local environment, which changes the distribution of observations at sensors located within the realm of this event. We propose a method for performing sequential multiple change-point detection under the Bayesian paradigm. It is shown analytically that the proposed procedure controls the false discovery rate (FDR), which is an appropriate criterion for statistically controlling the prevalence of false alarms in a setting where multiple decisions are made in parallel. It is numerically shown that exploiting spatial information decreases the average detection delay compared to procedures that do not properly use this information.

Original languageEnglish
Pages (from-to)4515-4519
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 1 Jan 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021


  • Bayesian change-point detection
  • False discovery rate
  • Multiple hypothesis testing
  • Propagating events
  • Sensor network

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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