A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

2019 ◽  
Vol 21 (4) ◽  
pp. 1659-1670
Author(s):  
José Melo ◽  
Aníbal Matos
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Data Driven

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

2020 ◽  
Vol 42 (11) ◽  
pp. 1946-1959
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Jian Cao ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Gaussian Kernel ◽  
Filter Method ◽  
Grey Prediction ◽  
Sea Trial ◽  
False Diagnosis

The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

scholarly journals PROPELLER FAULT DIAGNOSIS BASED ON A RANK PARTICLE FILTER FOR AUTONOMOUS UNDERWATER VEHICLES

Brodogradnja ◽  
2018 ◽  
Vol 69 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Jiayu He ◽  
◽  
Ye Li ◽  
Yanqing Jiang ◽  
Yueming Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

Polynomial regressors based data-driven control for autonomous underwater vehicles

2020 ◽  
Vol 13 (5) ◽  
pp. 1767-1775
Author(s):  
Hongran Li ◽  
Weiwei Xu ◽  
Heng Zhang ◽  
Jian Zhang ◽  
Yi Liu
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Data Driven
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