Fast Federated Kalman Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

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Weighted Measurement Fusion Particle Filter for Nonlinear Systems with Correlated Noises

Sensors ◽

10.3390/s18103242 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3242 ◽

Cited By ~ 1

Author(s):

Ke Wei Zhang ◽

Gang Hao ◽

Shu Li Sun

Keyword(s):

Nonlinear Systems ◽

Particle Filter ◽

Series Expansion ◽

Taylor Series ◽

Asymptotic Optimality ◽

Taylor Series Expansion ◽

Full Rank ◽

Expansion Method ◽

Least Square ◽

Correlated Noises

The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm.

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Finite element-based observer design for nonlinear systems with delayed measurements

Kybernetika ◽

10.14736/kyb-2019-6-1050 ◽

2020 ◽

pp. 1050-1069

Author(s):

Branislav Rehák

Keyword(s):

Finite Element ◽

Nonlinear Systems ◽

Observer Design ◽

Delayed Measurements

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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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Tracking: B

Probability in Electrical Engineering and Computer Science ◽

10.1007/978-3-030-49995-2_10 ◽

2021 ◽

pp. 193-204

Author(s):

Jean Walrand

Keyword(s):

Kalman Filter ◽

Nonlinear Systems ◽

Extended Kalman Filter ◽

Random Variable

AbstractIn Chapter Tracking: A, we explained the estimation of a random variable based on observations. We also described the Kalman filter and we gave a number of examples. In this chapter, we derive the Kalman filter and explain some of its properties. We also discuss the extended Kalman filter.Section 10.1 explains how to update an estimate as one makes additional observations. Section 10.2 derives the Kalman filter. The properties of the Kalman filter are explained in Sect. 10.3. Section 10.4 shows how the Kalman filter is extended to nonlinear systems.

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