Detecting Non-Overlapping Signals with Dynamic Programming

Mordechai Roth, Amichai Painsky*, Tamir Bendory

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained likelihood optimization problem and design a computationally efficient dynamic program that attains its optimal solution. Our proposed framework is scalable, simple to implement, and robust to model uncertainties. We show by extensive numerical experiments that our algorithm accurately estimates the locations in dense and noisy environments, and outperforms alternative methods.

Original languageEnglish
Article number250
JournalEntropy
Volume25
Issue number2
DOIs
StatePublished - Feb 2023

Keywords

  • detection theory
  • dynamic programming
  • gap statistics

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