Multi-Sensor Integrated Navigation Algorithm Using Adaptive Federated Kalman Filter for MAVs

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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

10.21203/rs.3.rs-1111330/v1 ◽

2021 ◽

Author(s):

Qinghua Luo ◽

Xiaozhen Yan ◽

Chenxu Wang ◽

Yang Shao ◽

Zhiquan Zhou ◽

...

Keyword(s):

Kalman Filter ◽

Autonomous Underwater Vehicle ◽

Integrated Navigation ◽

Multiple Sources ◽

Random Drift ◽

Position Information ◽

Federated Kalman Filter ◽

Ocean Environment ◽

Important Position ◽

Multiple Resource

Abstract 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.

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A New Adaptive Federated Kalman Filter for the Multi-sensor Integrated Navigation System of MAVs

2018 13th World Congress on Intelligent Control and Automation (WCICA) ◽

10.1109/wcica.2018.8630355 ◽

2018 ◽

Author(s):

Ge-Hang Lei ◽

Zhe-Jun Fang ◽

Bing Luo ◽

Pei-Feng Qi

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Federated Kalman Filter

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New fault-tolerant federated Kalman filters for integrated navigation

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-12-2013-0227 ◽

2018 ◽

Vol 90 (1) ◽

pp. 65-73

Author(s):

Yueqian Liang ◽

Yingmin Jia

Keyword(s):

Kalman Filter ◽

Kalman Filters ◽

Fault Tolerant ◽

Distribution Coefficients ◽

Integrated Navigation ◽

Content Type ◽

Vector Distribution ◽

Federated Kalman Filter ◽

Aerial Vehicle ◽

Navigation Accuracy

Purpose The purpose of this paper is to achieve accurate integrated navigation results for the unmanned aerial vehicle (UAV) systems even in the presence of possible navigation faults in the subsystems of the federated Kalman filter. Design/methodology/approach The federated Kalman filter is modified from two aspects to get accurate navigation results under abnormity. First, time-variant vector distribution coefficients trading off the navigation accuracy and the observability degree of each state component are computed to replace the traditional scalar coefficients. Second, a fault-tolerant filter is proposed as the local navigation filter. Findings Simulations for the navigation of a UAV system show that the proposed method can be applied for accurate navigation purpose even in the presence of subsystem navigation faults. Originality/value New fault-tolerant federated Kalman filters for integrated navigation are presented to achieve accurate navigation solutions.

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Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.1544 ◽

2013 ◽

Vol 347-350 ◽

pp. 1544-1548

Author(s):

Zi Yu Li ◽

Yan Liu ◽

Ping Zhu ◽

Cheng Ying

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Navigation System ◽

Integrated System ◽

Navigation Systems ◽

Integrated Navigation ◽

Fusion Algorithm ◽

Integrated Navigation System ◽

Non Linear ◽

Federated Kalman Filter

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

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