scholarly journals A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

2021 ◽  
Author(s):  
Di Liu ◽  
Xiyuan Chen ◽  
Xiao Liu
Keyword(s):  
Data Fusion ◽  
Navigation System ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

Application of Fuzzy Adaptive Fusion Algorithm in INS/BNS/GPS Integrated Navigation System

2012 ◽  
Vol 232 ◽  
pp. 205-209
Author(s):  
Yan Ren ◽  
Duan Xu ◽  
Wei Feng Yue
Keyword(s):  
Data Fusion ◽  
Global Positioning System ◽  
Navigation System ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Adaptive Fusion ◽  
Fuzzy Fusion ◽  
Fuzzy Adaptive

The problem of data fusion based on filter is studied for an integrated inertial navigation system / Beidou navigation system / global positioning system (INS/BNS/GPS) with uncertain noise and conditionality of using GPS. The integrated navigation system can be divided into two integrated navigation subsystems (INS/BNS and INS/GPS). The signals from GPS and BNS receivers are easy to be disturbed, so filter is used to estimate the subsystem errors which are transmitted to fusion center online. Then data fusion is carried out by using the fuzzy fusion algorithm. Simulation results show that the algorithm can improve the accuracy and stability of navigation system.

Study on Data Fusion Algorithm in High Dynamic GPS/INS Integrated Navigation System

2012 ◽  
Vol 220-223 ◽  
pp. 2280-2283
Author(s):  
You Yi Ye ◽  
Xiang Zhao
Keyword(s):  
Data Fusion ◽  
Navigation System ◽  
Dynamic Environment ◽  
State Equation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
High Dynamic ◽  
Range Rate

This document explains how to aim at low-precision in navigation and position of Inertial Navigation System and dependence of Global Positioning System. A combination of these two systems provides good compensations for each other. GPS/INS integrated navigation system in high dynamic environment is studied, an integrated algorithm based on the pseudo-range and pseudo-range rate is introduced. By analyzing the error model of INS and GPS navigation systems, the state equation and the observation equation are founded. Finally, data fusion algorithm is simulated using kalman filtering algorithm, simulation results show that the data fusion algorithm can improve reliability and maturity of integrated navigation system.

scholarly journals Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yuan Xu ◽  
Xiyuan Chen ◽  
Qinghua Li
Keyword(s):  
Data Fusion ◽  
Navigation System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Inertial Navigation Systems ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Iterated Extended Kalman Filter ◽  
The Mean ◽  
Noise Statistics

As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.

Study of DR/GPS Integrated Navigation on Nonlinear Adaptive Data Fusion Algorithm

2013 ◽  
Vol 823 ◽  
pp. 317-320
Author(s):  
Meng Long Cao ◽  
Shu Mei Yao
Keyword(s):  
Nonlinear System ◽  
Data Fusion ◽  
Information Fusion ◽  
Navigation System ◽  
State Vector ◽  
High Order ◽  
Experimental Results ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

Aiming at collecting data fusion problem for the actual project integrated navigation system,this thesis propounds the system measured mathematics models and proposes adaptive information fusion algorithm based on nonlinear system. The proposed method considers system unmodelled part and high order item as the noise item and the state vector to coupled estimated,thus the sensitivity of the algorithm to the model is improved. The effect of the improved algorithm is tested by the simulation in the environment of Matlab. The experimental results demonstrate that this algorithm can improve the accuracy of the integrated navigation system, thus has the value of practice application.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

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.

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