Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo
This addresses parameter estimation for multi-chirp signals in noisy environments, which is an incremental improvement for signal processing applications.
The paper tackles the problem of estimating parameters from noisy mixtures of higher-order polynomial chirps by formulating it as non-convex optimization and proposing a modified Langevin Monte Carlo sampler that uses average curvature guidance. Results show the CG-LMC algorithm is robust and succeeds in low SNR regimes, making it viable for practical applications.
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.