Analysis of Various Kalman Filter Algorithms using Different Inertial Navigation Systems Integrated with the Global Positioning System

2006 ◽  
Vol 52 (2) ◽  
pp. 59-67 ◽  
Author(s):  
N. El-Sheimy ◽  
S. Nassar ◽  
E.-H. Shin ◽  
X. Niu
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Navigation ◽  
Positioning System ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Global Positioning ◽  
Filter Algorithms

Integration of Satellite and Inertial Positioning Systems

1990 ◽  
Vol 43 (1) ◽  
pp. 48-57 ◽  
Author(s):  
M. Napier
Keyword(s):  
Global Positioning System ◽  
System Integration ◽  
System Performance ◽  
Inertial Navigation ◽  
Positioning System ◽  
Navigation Systems ◽  
Relative Positioning ◽  
Inertial Navigation Systems ◽  
Positioning Systems ◽  
Global Positioning

The Global Positioning System (GPS) offers an absolute positioning accuracy of 15 to 100 metres. Inertial navigation complements GPS in that it provides relative positioning and is totally self-contained. These two positioning sensors are ideally suited for system integration for although there is not necessarily an improvement in accuracy, the integration of GPS with inertial navigation systems (INS) does enable an increase in system performance.

scholarly journals MODEL SISTEM NAVIGASI INERSIAL: SEBUAH TINJAUAN

Oseanika ◽  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhamad Irfan ◽  
Dwi Haryanto
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Global Navigation Satellite System ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Positioning System ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Global Positioning ◽  
Global Navigation Satellite

Sistem navigasi merupakan sistem yang memandu wahana gerak dari satu tempat ke tempat lainnya. Ada banyak sistem navigasi yang digunakan baik untuk kepentingan survei maupun untuk kepentingan umum. Sistem navigasi yang sudah dikenal luas adalah sistem navigasi berbasis satelit menggunakan global navigation satellite system (GNSS) atau global positioning system (GPS). GPS mempunyai kelemahan akibat faktor eksternal yakni sangat tergantung pada perambatan sinyal gelombang elektromagnetik dari satelit GPS ke receiver GPS. Sistem navigasi yang lainnya dan belum banyak dikenal namun sudah banyak digunakan adalah sistem navigasi inersial atau INS (inertial navigation system). INS ini merupakan sistem navigasi yang tidak terpengaruh oleh faktor eksternal, karena dibuat dengan mengikuti hukum gerak Newton, dan terdiri dari sensor accelerometer dan gyroscope. Biasanya INS ini dikombinasikan dengan sistem navigasi GPS untuk mendapatkan informasi navigasi yang lengkap dan akurat, yaitu posisi absolut, percepatan, kecepatan, arah, dan kelabilan (attitude) dengan frekuensi pengambilan data yang tinggi. Tulisan ini membahas tentang model dasar INS dari buku “Inertial Navigation Systems with Geodetic Applications” [Jekeli].Kata kunci:navigasi, accelerometer, gyroscope, inersial, GPS, Kalman filter

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Supervised Machine Learning ◽  
Positioning System ◽  
Position Information ◽  
Global Positioning

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

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