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

• URL: http://arxiv.org/abs/2501.18178v1
• Date: Thu, 30 Jan 2025 07:15:38 GMT
• Title: Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo
• Authors: Sattwik Basu, Debottam Dutta, Yu-Lin Wei, Romit Roy Choudhury,
• Abstract summary: This paper considers the problem of parameters from a noisy mixture of chirps.<n>We propose a modified Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the objective function.
• Score: 7.832209959041259
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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