Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo
Signal Processing
2025-01-31 v1 Machine Learning
Machine Learning
Abstract
This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a modified Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the objective function to reliably find the minimizer. Results show that our Curvature-guided LMC (CG-LMC) algorithm is robust and succeeds even in low SNR regimes, making it viable for practical applications.
Keywords
Cite
@article{arxiv.2501.18178,
title = {Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo},
author = {Sattwik Basu and Debottam Dutta and Yu-Lin Wei and Romit Roy Choudhury},
journal= {arXiv preprint arXiv:2501.18178},
year = {2025}
}