Study on Information Fusion Algorithm in Embedded Integrated Navigation System

2008 ◽  
Author(s):  
Jian Wang ◽  
Jiang Liu ◽  
Bo-gen Cai
Keyword(s):  
Information Fusion ◽  
Navigation System ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

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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