Fault Tolerant Multi-Sensor Federated Filter for AUV Integrated Navigation

Guo, Shuai; He, Bo; Feng, Chen; Liu, Hanmin; Yin, Feng; Zhang, Xin; Mu, Xiaokai; Li, Tengyue; Yan, Tianhong

doi:10.1109/ut.2019.8734279

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…2 Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic, Guilin, China. 3 Shandong New Beiyang Information Technology Co., Ltd., Weihai, China.…”

Section: Abbreviationsmentioning

confidence: 99%

“…In marine-related research fields, autonomous underwater vehicle (AUV) is applied widely to both civil and military-strategic areas [1,2]. And navigation or positioning information is the key for the AUV to figure out where it is and where to go [2][3][4]. The navigation and positioning system determines the efficiency and accuracy when AUV performs underwater missions [5].…”

Section: Introductionmentioning

confidence: 99%

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A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

et al. 2022

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The navigation and positioning subsystem offers important position information for an autonomous underwater vehicle (AUV) system. It plays a crucial role during the underwater exploration and operations of AUV. Many scholars research underwater navigation and positioning. Various improved methods and systems were presented. However, as the diversity of the ocean environment, the random drift of the gyroscope, error accumulation, the variety of tasks, and other negative factors, the navigation and positioning results are uncertain and incredible. The accuracy, stability, and robustness are not guaranteed, which cannot meet the increasing application requirement. Therefore, we put forward a SINS/DVL/USBL integrated navigation and positioning IoT system with multiple resource fusion and a federated Kalman filter. In this method, we first present an improved SINS/DVL combined subsystem with a filtering gain compensation strategy. So we can enhance the accuracy and stability of the navigation and position system. Secondly, we proposed a USBL positioning subsystem with the Kalman filtering acoustic signals to improve USBL positioning performance. Lastly, we present a federated Kalman filter to fuse the positioning information from the SINS/DVL combined positioning subsystem and the USBL positioning subsystem. Through the three methods, we can enhance the positioning accuracy and robustness. Comprehensive simulation results indicated the feasibility and effectiveness of the proposed SINS/DVL/USBL integrated navigation and positioning system, which provides critical reference for other positioning method, and it also offers crucial position information for AUV to achieve high accuracy and efficiency tasks.

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Section: Abbreviationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

et al. 2022

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“…Sensor measurement mechanism, seawater medium, and complex underwater environment impose great challenges on multi-source information fusion (Prasanth, 2021; Song et al, 2022). The well-known federated Kalman filter has always been mostly used for the multi-source information fusion among the stand-alone INS and aiding sensors of an underwater vehicle (Guo et al, 2019). However, it also has inevitable drawbacks in the practical application.…”

Section: Introductionmentioning

confidence: 99%

A post-processing algorithm based on factor graph optimization for autonomous underwater vehicle integrated navigation

Liu

et al. 2022

Transactions of the Institute of Measurement and Control

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Aiming at the requirements of offline high-precision navigation and positioning in the mapping of ocean floor, survey, and other fields for autonomous underwater vehicle (AUV)integrated navigation system, a post-processing algorithm based on factor graph optimization is proposed in this paper. First, the factor graph model is established by the factor graph formulation to address the asynchronous and heterogeneous problem of multi-source information fusion. Then the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU factors between two adjacent measurements to further aid high-rate navigation solutions. Second the forward and backward message passing processes are performed independently using the sum-product algorithm in the factor graph model. Finally, the optimal navigation solution is obtained by weighted smoothing the results of the forward and backward message passing processes. Simulation results show this algorithm proposed in this paper provides more accurate navigation accuracy and smoothness, especially in the case of signals loss or sensors fault. Compared with the federal Kalman filtering method, the horizontal positioning errors of the un-simplified factor graph, the simplified factor graph, and the factor graph optimization are improved by about 30%, 34%, and 80%, respectively. The semi-physical environment results also clearly verify the reliability and effectiveness of the proposed method, and its horizontal positioning accuracies are kept within −2∼2 m.

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“…In marine-related research fields, autonomous underwater vehicle (AUV) is widely applied to both civil and military-strategic areas [1][2]. And navigation or positioning information is the key for the AUV to find out where it is and where to go [2][3][4]. The navigation and positioning system determines the efficiency and accuracy when AUV performs underwater missions [5].…”

Section: Introductionmentioning

confidence: 99%

“…Fig 3. The framework of the underwater positioning(1) Array Element Setup The non-equidistant quaternary array of the USBL system is composed as shown in Figure4.…”

mentioning

confidence: 99%

A SINS/DVL/USBL Integrated Navigation and Positioning IoT System with Multiple Sources Fusion and Federated Kalman Filter

Luo

Yan

Wang

et al. 2021

Preprint

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The navigation and positioning subsystem offers important position information for an autonomous underwater vehicle (AUV) system. It plays a crucial role during the underwater exploration and operations of AUV. Many scholars research underwater navigation and positioning. And many promising methods and systems were presented. However, as the diversity of ocean environment, the random drift of the gyroscope, error accumulation, the diversity of tasks, and other negative factors, the navigation and positioning result is uncertain and incredible. The accuracy, stability and robustness are not guaranteed, which can not meet the increasing application requirement. Therefore, we put forward a SINS/DVL/USBL integrated navigation and positioning IoT system with multiple resource fusion and a federated Kalman filter. In this method, we first present an improved SINS/DVL combined subsystem with filtering gain compensation strategy. The accuracy and stability of the navigation and position system can be enhanced. Secondly, We proposed a USBL positioning subsystem with the Kalman filtering acoustic signals to improve USBL positioning performance. Lastly, we present a federated Kalman Filter to fuse the positioning information from the SINS/DVL combined positioning subsystem and the USBL positioning subsystem. Through the above three methods, we can improve the positioning accuracy and robustness. Comprehensive simulation results indicated the feasibility and effectiveness of the proposed SINS/DVL/USBL integrated navigation and positioning system.

show abstract

Fault Tolerant Multi-Sensor Federated Filter for AUV Integrated Navigation

Cited by 11 publications

References 7 publications

A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

A post-processing algorithm based on factor graph optimization for autonomous underwater vehicle integrated navigation

A SINS/DVL/USBL Integrated Navigation and Positioning IoT System with Multiple Sources Fusion and Federated Kalman Filter

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