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

Luo, Qinghua; Yan, Xiao; Wang, Chenxu; Shao, Yang; Zhou, Zhiquan; Li, Jianfeng; Hu, Cong; Wang, Chuntao; Ding, Jinfeng

doi:10.1186/s13677-022-00289-3

Cited by 11 publications

(7 citation statements)

References 26 publications

(42 reference statements)

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“…The federated Kalman filter is a distributed data fusion algorithm based on information allocation, which has been widely used in multi-sensor data fusion fields, including integrated navigation. 8 The main idea is that the whole system is divided into several local filtering systems, and a primary filter is designed for these systems. The main structure of the filter is a cascaded decentralized filtering structure composed of several local filters and a main filter.…”

Section: Methodsmentioning

confidence: 99%

Sanda training program based on multi‐sensor data fusion in the internet of things environment

Zhou

2023

Internet Technology Letters

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Sanda, also known as Chinese kickboxing, is a Wushu that combines elements of traditional Kung Fu, kickboxing, and grappling. Considering that the use of action recognition can effectively improve the level of Sanda players, this study uses inertial sensors and visual sensors to collect the motion data of Sanda. A federated Kalman filter with time‐varying parameters is designed according to the error form of each sensor to complete the data fusion. The main idea is that the whole system is divided into several local filtering systems, and a primary filter is designed for these systems. The main structure of the filter is a cascaded decentralized filtering structure composed of several local filters and a primary filter. The experimental results show that the proposed Sanda training scheme based on multi‐sensor data fusion under the Internet of Things environment has high action recognition accuracy, and the joint inertial‐vision model produces significantly better results than the other two models.

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Section: Methodsmentioning

confidence: 99%

Sanda training program based on multi‐sensor data fusion in the internet of things environment

Zhou

2023

Internet Technology Letters

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“…This algorithm combines multiple decisions made by different sensors and finally makes a fusion decision. Luo et al [ 19 ] proposed a joint Kalman filter to fuse the positioning information. This information is the combined localization subsystem.…”

Section: Related Workmentioning

confidence: 99%

A task level fusion autonomous switching mechanism

Lv,

Wang,

Zhang

2023

PLoS ONE

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Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

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“…Considering the advantages of USBL and DVL, respectively, an INS/DVL/USBL integrated navigation system is a feasible scheme for Autonomous Underwater vehicles (AUV). Integrated navigation filtering methods can be divided into centralized filtering [ 15 ] and federal filtering [ 16 ]. In [ 15 ], a grid INS/USBL/DVL integrated navigation algorithm for polar navigation based on centralized filtering was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…A vector fault detection method was designed to improve the robustness of the system. Different from the centralized filtering, an INS/USBL/DVL integrated navigation model based on federal filtering is proposed in [ 16 ]. To isolate the faulty sub-filter, it improves its fault tolerance.…”

Section: Introductionmentioning

confidence: 99%

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

Liu

Yan

et al. 2023

Sensors

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The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.

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

Cited by 11 publications

References 26 publications

Sanda training program based on multi‐sensor data fusion in the internet of things environment

Sanda training program based on multi‐sensor data fusion in the internet of things environment

A task level fusion autonomous switching mechanism

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

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