  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.
