Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

INS/Odometer Integration: Positional Approach

2021 ◽  
Vol 29 (2) ◽  
pp. 110-125
Author(s):  
A.A. Golovan ◽  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Zero Velocity ◽  
Strapdown Inertial Navigation

The problem of a strapdown inertial navigation system (SINS) integration with an odometer as part of an integrated navigation system is considered. The odometer raw measurement is considered as an increment of the distance traveled along the odometer ‘measuring’ axis. Models of the integration solution components for the case of threedimensional navigation are presented, among which are the models of inertial autonomous and kinematic odometer dead reckoning (DR), models of relevant error equations, the model of SINS position aiding based on the odometer DR data and using GNSS position and velocity, wherever possible. The models comprise objective components, which do not depend on the type of the inertial sensors used and their accuracy grade, and variable components, which take into account the properties of the navigation sensors used. The integration does not require zero velocity updates, known as ZUPT correction, which are commonly used in navigation application.

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 Airborne Integrated Navigation System Based on SINS/GPS/TAN/EOAN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junjun Tang ◽  
Peijuan Li
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Integrated Navigation ◽  
Long Distance ◽  
Integrated Navigation System ◽  
Long Time ◽  
Federated Kalman Filter ◽  
Entropy Weighted ◽  
Navigation Errors ◽  
Navigation Information

Considering the drawbacks that GPS signal is susceptible to obstacles and TAN becomes useless in some area when without any terrain data or with a featureless terrain field, to realize long-distance and high-precision navigation, a navigation system based on SINS/GPS/TAN/EOAN is presented. When GPS signal is available, GPS is used to correct errors of SINS; when GPS is unavailable, a terrain selection method based on the entropy weighted gray relational decision-making method is use to distinguish terrain into matchable areas and unmatchable areas; then, for the matchable areas, TAN is used to correct errors of SINS, for the unmatchable areas, EOAN is used to correct errors of SINS. The principles of SINS, GPS, TAN, and EOAN are analyzed, the mathematic models of SINS/GPS, SINS/TAN, and SINS/EOAN are constructed, and finally the federated Kalman filter is used to fuse navigation information. Simulation results show that the trajectory of SINS/GPS/TAN/EOAN is close to the ideal one in both matchable area or unmatchable area and whose navigation errors are obviously reduced, which is important for the realization of long-time and high-precision positioning.

scholarly journals Improved Underwater Integrated Navigation System using Unscented Filtering Approach

2015 ◽  
Vol 69 (3) ◽  
pp. 561-581 ◽  
Author(s):  
Mohammad Shabani ◽  
Asghar Gholami
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Models ◽  
Error State ◽  
Filtering Approach

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.

Estimating Gyro Drift of Strapdown Inertial Navigation System Based on Star Sensor

2012 ◽  
Vol 566 ◽  
pp. 235-238
Author(s):  
Guang Tao Zhou ◽  
Gui Min Shi ◽  
Lei Zhang ◽  
Kai Li
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Model Simulation ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Star Sensor ◽  
Integrated Navigation ◽  
Working Principle ◽  
Gyro Drift ◽  
Strapdown Inertial Navigation

In the strapdown inertial navigation system (SINS), gyro drift will result in navigation errors. A new algorithm based on star sensor is proposed in this paper to estimate gyro drift. The paper analyzed the working principle of star sensor and the technique of estimating gyro drift. Gyro drift can be estimated through the high-precision attitude information provided by a star sensor. Kalman filter is used in the integrated navigation model. Simulation results show that the proposed algorithm can estimate gyro drift accurately and improve the precision of SINS.

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