Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter
The paper focuses on the optimal state-estimation algorithm for discrete-continuous systems. The research aim is to create an effective strategy for combining data from continuous and discrete information sources to improve the state estimation accuracy and reliability of complex dynamic systems. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/933 |
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| author | Vadim Kramar Kirill Dementiev Aleksey Kabanov |
| author_facet | Vadim Kramar Kirill Dementiev Aleksey Kabanov |
| author_sort | Vadim Kramar |
| collection | DOAJ |
| description | The paper focuses on the optimal state-estimation algorithm for discrete-continuous systems. The research aim is to create an effective strategy for combining data from continuous and discrete information sources to improve the state estimation accuracy and reliability of complex dynamic systems. The paper discusses, in detail, the theoretical foundations of the proposed method, including the mathematical description of continuous and discrete models, and its optimality criterion formulation. State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. The conclusions are drawn about the promising application for the developed algorithm in various fields related to information processing in complex technical systems, such as navigation, motion control, and state and processes monitoring. It is noted that the proposed approach can be generalized to the case of more sources’ fusion. The paper is considered to be valuable for specialists in control theory and signal and information processing, as well as for navigation and motion-control system designers. The results obtained may find practical application in the development of high-precision state-estimation systems in various technical applications. |
| format | Article |
| id | doaj-art-7d60a3bf4fce4906beb3f2be9ea3ca5e |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-7d60a3bf4fce4906beb3f2be9ea3ca5e2025-08-20T02:34:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113593310.3390/jmse13050933Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman FilterVadim Kramar0Kirill Dementiev1Aleksey Kabanov2Robotics and Intelligent Control Systems Laboratory, Sevastopol State University, Sevastopol 299053, RussiaRobotics and Intelligent Control Systems Laboratory, Sevastopol State University, Sevastopol 299053, RussiaRobotics and Intelligent Control Systems Laboratory, Sevastopol State University, Sevastopol 299053, RussiaThe paper focuses on the optimal state-estimation algorithm for discrete-continuous systems. The research aim is to create an effective strategy for combining data from continuous and discrete information sources to improve the state estimation accuracy and reliability of complex dynamic systems. The paper discusses, in detail, the theoretical foundations of the proposed method, including the mathematical description of continuous and discrete models, and its optimality criterion formulation. State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. The conclusions are drawn about the promising application for the developed algorithm in various fields related to information processing in complex technical systems, such as navigation, motion control, and state and processes monitoring. It is noted that the proposed approach can be generalized to the case of more sources’ fusion. The paper is considered to be valuable for specialists in control theory and signal and information processing, as well as for navigation and motion-control system designers. The results obtained may find practical application in the development of high-precision state-estimation systems in various technical applications.https://www.mdpi.com/2077-1312/13/5/933continuous-discrete systemssensor fusionstate estimationoptimal estimationterminal systems |
| spellingShingle | Vadim Kramar Kirill Dementiev Aleksey Kabanov Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter Journal of Marine Science and Engineering continuous-discrete systems sensor fusion state estimation optimal estimation terminal systems |
| title | Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter |
| title_full | Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter |
| title_fullStr | Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter |
| title_full_unstemmed | Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter |
| title_short | Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter |
| title_sort | optimal state estimation in underwater vehicle discrete continuous measurements via augmented hybrid kalman filter |
| topic | continuous-discrete systems sensor fusion state estimation optimal estimation terminal systems |
| url | https://www.mdpi.com/2077-1312/13/5/933 |
| work_keys_str_mv | AT vadimkramar optimalstateestimationinunderwatervehiclediscretecontinuousmeasurementsviaaugmentedhybridkalmanfilter AT kirilldementiev optimalstateestimationinunderwatervehiclediscretecontinuousmeasurementsviaaugmentedhybridkalmanfilter AT alekseykabanov optimalstateestimationinunderwatervehiclediscretecontinuousmeasurementsviaaugmentedhybridkalmanfilter |