Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866929692427157504 |
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| author | Basu, Sattwik Dutta, Debottam Wei, Yu-Lin Choudhury, Romit Roy |
| author_facet | Basu, Sattwik Dutta, Debottam Wei, Yu-Lin Choudhury, Romit Roy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo Basu, Sattwik Dutta, Debottam Wei, Yu-Lin Choudhury, Romit Roy Signal Processing Machine Learning 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. |
| title | Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2501.18178 |