Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.21997 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908855060922368 |
|---|---|
| author | Tiwari, Ranjeet K. Sgarioto, Daniel Graham, Peter Skvortsov, Alexei Arulampalam, Sanjeev Ranasinghe, Damith C. |
| author_facet | Tiwari, Ranjeet K. Sgarioto, Daniel Graham, Peter Skvortsov, Alexei Arulampalam, Sanjeev Ranasinghe, Damith C. |
| contents | Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model Tiwari, Ranjeet K. Sgarioto, Daniel Graham, Peter Skvortsov, Alexei Arulampalam, Sanjeev Ranasinghe, Damith C. Signal Processing Artificial Intelligence Systems and Control Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge. |
| title | Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model |
| topic | Signal Processing Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2511.21997 |