AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter

ZHANG Xiao-fei; XIN Ming-zhen; SUI Hai-chen; LEI Peng; LIU Yi-cheng; YANG Fan-lin

doi:10.11993/j.issn.2096-3920.2022.01.004

Volume 30 Issue 1

Feb 2022

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Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(1): 29-36

ZHANG Xiao-fei, XIN Ming-zhen, SUI Hai-chen, LEI Peng, LIU Yi-cheng, YANG Fan-lin. AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter[J]. Journal of Unmanned Undersea Systems, 2022, 30(1): 29-36. doi: 10.11993/j.issn.2096-3920.2022.01.004

Citation:

ZHANG Xiao-fei, XIN Ming-zhen, SUI Hai-chen, LEI Peng, LIU Yi-cheng, YANG Fan-lin. AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter[J]. Journal of Unmanned Undersea Systems, 2022, 30(1): 29-36. doi: 10.11993/j.issn.2096-3920.2022.01.004

Citation:

ZHANG Xiao-fei, XIN Ming-zhen, SUI Hai-chen, LEI Peng, LIU Yi-cheng, YANG Fan-lin. AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter[J]. Journal of Unmanned Undersea Systems, 2022, 30(1): 29-36. doi: 10.11993/j.issn.2096-3920.2022.01.004

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AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter

doi: 10.11993/j.issn.2096-3920.2022.01.004

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
2Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China
3.
Tianjin Research Institute for Water Transport Engineering Ministry of Transport, Tianjin 300456, China

Received Date: 2021-01-09
Rev Recd Date: 2021-04-25
Publish Date: 2022-02-28

Abstract

Abstract

Owing to complex marine environments, the tracking and positioning of autonomous undersea vehicles (AUVs) that use ultra-short baseline may be affected by various errors, and a Kalman filter based on the minimum mean square error is usually used to process the dynamic positioning data. It is important to ensure the accuracy and reliability of the Kalman filtering to construct a motion model that matches the actual motion of the target. However, the AUV is characterized by strong maneuverability, which often renders it difficult to a priori determine a single motion model to achieve the matching of all motion states. To address the inability of the single-model based Kalman filter to adapt to all the motion states of an underwater target, an interactive multi-model Kalman filter(IMMKF) algorithm was used to process the ultra-short baseline tracking data of an AUV. Furthermore, a probability matrix transfer between motion models was used to enhance the adaptability of motion states. The experimental results showed that the IMMKF algorithm was better than the Kalman filter algorithm for a single model when the multi model set was constructed reasonably.
- autonomous undersea vehicle(AUV),
- ultra-short baseline,
- interactive multi-model,
- Kalman filter,
- motion model

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References(23)

References

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AUV Ultra-short Baseline Tracking Algorithm Based on Interactive Multi-Model Kalman Filter

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