Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

2012 ◽  
Vol 23 (2) ◽  
pp. 256-264 ◽  
Author(s):  
Honglei Qin ◽  
Li Cong ◽  
Xingli Sun
Keyword(s):  
Navigation System ◽  
Vehicle Navigation ◽  
Integrated Navigation ◽  
Accuracy Improvement ◽  
Integrated Navigation System ◽  
Land Vehicle ◽  
Land Vehicle Navigation

A Kalman Filter Model for an Integrated Land Vehicle Navigation System

1995 ◽  
Vol 48 (2) ◽  
pp. 293-302 ◽  
Author(s):  
Allison N. Ramjattan ◽  
Paul A. Cross
Keyword(s):  
Dead Reckoning ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Filter Model ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Vehicle Navigation System ◽  
Vehicle Position

Unlike in the case of airborne and offshore applications, GPS cannot be used continuously for land vehicle navigation due to the loss of satellite signals by obstructions from buildings, trees, etc. With the increasing trend in various sectors of the economy towards efficient fleet management, the challenges of providing a system capable of providing high-accuracy vehicle position and location anywhere, continuously, has led to renewed interest in the area of integrated navigation systems. In order to satisfy these conditions, an integrated system comprising GPS and gyro/odometer dead reckoning has been developed. This paper gives a description of the implemented system and shows some of the practical results that can be obtained using Kalman filtering algorithms.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

2014 ◽  
Vol 67 (6) ◽  
pp. 967-983 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Binghao Li ◽  
Jingxiang Gao ◽  
Xinglong Tan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Positioning Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Fuzzy Neural

The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

2004 ◽  
Vol 57 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Jau-Hsiung Wang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Urban Areas ◽  
Low Cost ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Artificial Neural Network Ann ◽  
Signal Fading

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

Map-Aided Integrity Monitoring of a Land Vehicle Navigation System

2012 ◽  
Vol 13 (2) ◽  
pp. 848-858 ◽  
Author(s):  
Nagendra R. Velaga ◽  
Mohammed A. Quddus ◽  
Abigail L. Bristow ◽  
Yuheng Zheng
Keyword(s):  
Navigation System ◽  
Vehicle Navigation ◽  
Integrity Monitoring ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Vehicle Navigation System

Performance Analysis of Medium Accuracy FOG-Based IMU Integrated with Optical Odometer for Land Vehicle Navigation

2012 ◽  
Vol 245 ◽  
pp. 334-339 ◽  
Author(s):  
Muhammad Ilyas ◽  
Ren Zhang ◽  
Qiu Shi Qian ◽  
Yun Chun Yang
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Fiber Optics ◽  
Low Cost ◽  
Measurement Noise ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Land Vehicle ◽  
Land Vehicle Navigation

The aim of this work is to research the feasibility of using optical odometer as the aided sensor for accuracy improvement of medium accuracy (FOG)-based IMU for land vehicle navigation. Usually GNSS is integrated with low cost SINS (strapdown inertial navigation systems) for land vehicle navigation but GNSS is not always reliable in land vehicle applications. The focus is on analysis of position and velocity accuracy of SINS/Odometer integration using close loop kalman filter. Integrated navigation algorithm for vehicle states estimation and correction have been designed and implemented. The measurement error model for odometer in navigation frame is developed for Kalman filter implementation. As the prior knowledge of measurement noise which represents the stochastic properties of odometer is not exactly known, so an adaptive Kalman filter (AKF) is also proposed for online estimation of the measurement noise matrix in order to improve the accuracy. For the performance analysis of the designed system, field test is carried out and results show that the accuracy of the medium accuracy fiber optics gyro (FOG)-based SINS is improved and the systems is capable for land vehicle navigation application

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