On Optimal Quantization in Sequential Detection

Michael Faus, Manuel S. Stein, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review


The problem of designing optimal quantization rules for sequential detectors is investigated. First, it is shown that this task can be solved within the general framework of active sequential detection. Using this approach, the optimal sequential detector and the corresponding quantizer are characterized and their properties are briefly discussed. In particular, it is shown that designing optimal quantization rules requires solving a nonconvex optimization problem, which can lead to issues in terms of computational complexity and numerical stability. Motivated by these difficulties, two performance bounds are proposed that are easier to evaluate than the true performance measures and are potentially tighter than the bounds currently available in the literature. The usefulness of the bounds and the properties of the optimal quantization rules are illustrated with two numerical examples.

Original languageEnglish (US)
Pages (from-to)4440-4453
Number of pages14
JournalIEEE Transactions on Signal Processing
StatePublished - 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Sequential detection
  • active detection
  • optimal quantization rules
  • performance bounds


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