Detecting non-overlapping signals with dynamic programming

TL;DR

This paper introduces a dynamic programming approach for detecting non-overlapping signals in noisy one-dimensional data, offering an efficient, scalable, and robust solution that outperforms existing methods.

Contribution

The paper presents a novel dynamic programming algorithm for non-overlapping signal detection, improving accuracy and efficiency over prior techniques.

Findings

Accurately estimates signal locations in dense, noisy environments

Outperforms alternative detection methods

Scalable and robust to model uncertainties

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.