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.

Seamless INS/GPS Integration Based on Support Vector Machines

2013 ◽  
Vol 336-338 ◽  
pp. 277-280 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Support Vector Machines ◽  
Global Positioning System ◽  
Navigation System ◽  
Superior Performance ◽  
Support Vector ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Vector Machines ◽  
Global Positioning

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

Research on Integrated Navigation Algorithm Based on ANFIS

2011 ◽  
Vol 88-89 ◽  
pp. 438-441
Author(s):  
Tian Lai Xu ◽  
Yang Tian
Keyword(s):  
Global Positioning System ◽  
Navigation System ◽  
Fuzzy Inference ◽  
Integrated Navigation ◽  
Inference System ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Neuro Fuzzy ◽  
Global Positioning ◽  
Navigation Method

Combination of Global Positioning System (GPS) and Inertial Navigation System (INS) can improve the navigation performance that is superior to either one. This paper proposed and discussed an INS/GPS integrated navigation method based on adaptive neuro-Fuzzy Inference System (ANFIS) to fuse INS and GPS data. In this method, an ANFIS network was trained to mimic the error dynamical model of INS when GPS signals were available. If GPS outages occur, the trained ANFIS network is utilized to bridge the GPS outages. Simulations in INS/GPS integrated navigation system show the proposed method can reduce the positioning error during GPS outages.

scholarly journals An Enhanced GPS/INS Integrated Navigation System with GPS Observation Expansion

2016 ◽  
Vol 69 (5) ◽  
pp. 1041-1060 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Jingxiang Gao
Keyword(s):  
Global Positioning System ◽  
Adaptive Filter ◽  
Navigation System ◽  
Sampling Rate ◽  
Slow Motion ◽  
Integrated Navigation ◽  
State Variables ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Gps Observation

In Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation, the low sampling rate of GPS receivers reduces the observability of state variables. GPS observation expansion is proposed to enhance the GPS/INS integrated navigation system. During the process of observation expansion, the state variables are updated by the same GPS information repeatedly. According to uncertainty theory, the probability density function of GPS observation information is analysed to demonstrate the feasibility of GPS observation expansion. The formula and calculation method of an adaptive filter algorithm are presented to control the uncertainty of GPS observation expansion. Furthermore, an experiment is performed to validate the new algorithm. The results indicate that compared with GPS/INS integrated navigation without observation expansion, the enhanced GPS/INS integrated navigation system can improve the position, velocity and attitude accuracy significantly, especially while a land vehicle is in slow motion. At the same time, the adaptive filter factor is introduced into the new algorithm, which can control the uncertainty caused by the expanded GPS observation.

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.

INS/GPS/MNS Integrated Navigation System with Federated Kalman Filtering

2013 ◽  
Vol 718-720 ◽  
pp. 1207-1212
Author(s):  
De Ning Jiang ◽  
Tulu Muluneh Mekonnen
Keyword(s):  
Global Positioning System ◽  
Kalman Filtering ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Heading Angle ◽  
Adaptive Extended Kalman Filter

A multi-sensor integrated solution that combine complementary features of the Global Positioning System (GPS), inertial navigation system (INS), and magnetometer is presented due to GPS-aided inertial navigation system (INS) provides poor observability of heading angle. In addition, Based on the principle of federated Kalman filtering and Adaptive Extended Kalman Filter, the algorithm is presented also for accuracy of positioning and attitude, rapidity, and error tolerance of the navigation system. The algorithm is implemented in the integrated navigation system. Experimental results show that the observability issue is solved and improvement in accuracy.

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.

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