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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Detecting changes in real-time data: a user's guide to optimal detection.

P Johnson1, J Moriarty2, G Peskir3

  • 1School of Mathematics, University of Manchester, Manchester, UK peter.johnson-3@manchester.ac.uk.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|October 21, 2017
PubMed
Summary
This summary is machine-generated.

This review covers optimal detection theory for real-time signal change detection. It explores Bayesian and classical methods for minimizing detection delay while ensuring accuracy in noisy environments.

Keywords:
average detection delaychange-pointfalse alarmhypothesis testingoptimalityquickest detection

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Area of Science:

  • Applied science and engineering
  • Signal processing
  • Optimization theory

Background:

  • Real-time detection of signal changes is crucial in applied science and engineering.
  • Optimal detection theory, originating in the 1930s, addresses this challenge.
  • Applications span radar, sonar, communications, and power systems.

Purpose of the Study:

  • To review developments in optimal detection theory and sequential analysis.
  • To cover sequential hypothesis testing and change-point detection.
  • To discuss both Bayesian and classical settings.

Main Methods:

  • Review of parametric optimal detection theory.
  • Analysis in discrete and continuous time settings.
  • Discussion of Bayesian and non-Bayesian approaches.

Main Results:

  • Presentation of different measures of detection delay and their optimal solutions.
  • Emphasis on the role of signal-to-noise ratio.
  • Discussion of underlying assumptions and applications.

Conclusions:

  • Optimal detection theory provides essential frameworks for signal change detection.
  • The choice of delay measure and methodology depends on specific application constraints.
  • Further developments include stochastic calculus for continuous-time problems.