Fault-Tolerant Integrated Navigation Algorithm of the Federal Kalman Filter

2012 ◽  
pp. 621-627 ◽  
Author(s):  
Jing Li ◽  
Jiande Wu ◽  
Junfeng Hou ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Kalman Filter ◽  
Fault Tolerant ◽  
Integrated Navigation ◽  
Navigation Algorithm

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

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.

scholarly journals A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosu Xu ◽  
Peijuan Li ◽  
Jian-juan Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Fault Tolerant ◽  
Doppler Velocity ◽  
Observation Data ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Data Set ◽  
Integrated Navigation System ◽  
Short Period

The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

New fault-tolerant federated Kalman filters for integrated navigation

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.

scholarly journals RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 1704
Author(s):  
Xile Gao ◽  
Haiyong Luo ◽  
Bokun Ning ◽  
Fang Zhao ◽  
Linfeng Bao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Noise Covariance

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

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