Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo

Sattwik Basu; Debottam Dutta; Yu-Lin Wei; Romit Roy Choudhury

ICASSP 2025accepted0 citations

Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo

Sattwik Basu, Debottam Dutta, Yu-Lin Wei, Romit Roy Choudhury

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.

BibTeX

@inproceedings{icassp2025_estimatingmultic,
  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},
  booktitle = {ICASSP 2025},
  year = {2025}
}