Year: 2025
Type: conference-paper
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.
Authors: Sattwik Basu, Debottam Dutta, Yu-Lin Wei, Romit Roy Choudhury
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: CNS Core: Small: Collaborative Research: A Motion Tracking Library for Sports Analytics using Wireless Sensors, CNS Core: Small: Collaborative Research: loTScope: Sensing Physical Materials via Inexpensive loT Radios, RINGS: Provably Robust Machine Learning for Next Generation Cellular Networks, MRI: Acquisition of Omnipercipient Chamber for Gathering Ground Truth and Enabling Research on Smart and Connected Things
OpenAlex ID: W4408352661