Error estimation of INS/GPS integrated system based on PMI extended Kalman filter

2016 ◽  
Author(s):  
Xuan Zhao ◽  
Maiying Zhong ◽  
Dingfei Guo ◽  
Jia Guo
Keyword(s):  
Kalman Filter ◽  
Error Estimation ◽  
Extended Kalman Filter ◽  
Integrated System

Application of Extended Kalman Filter Algorithm in SDINS/GPS Integrated Inertial Navigation System

2013 ◽  
Vol 367 ◽  
pp. 528-535
Author(s):  
Otman Ali Awin
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Matlab Package ◽  
Noisy Measurements

This paper deals with the integrated navigation system based on fusion of data from Strap Down Inertial Navigation System (SDINS) and from Global Position System (GPS). In order to increase the accuracy and reliability of navigation algorithms, these two different systems are combined. The navigation system that be analyzed is basically of INS type while GPS corrective data are obtained less frequently and these are treated as noisy measurements in an extended Kalman filter scheme. The simulation of whole system (SDINS/GPS integrated system with Kalman filter) was modeled using MATLAB package, SIMULINK© tool. The proper choice of Kalman filter parameters had taken to minimize navigation errors for a typical medium range flight scenario (Simulated test trajectory and real trajectory of vehicle motion). A prototype of a SDINS installed on a moving platform in the laboratory to collected data by many experiments to verification our SIMULINK models.

scholarly journals An Extended Kalman Filter Algorithm for Integrating GPS and Low Cost Dead Reckoning System Data for Vehicle Performance and Emissions Monitoring

2003 ◽  
Vol 56 (2) ◽  
pp. 257-275 ◽  
Author(s):  
L. Zhao ◽  
W. Y. Ochieng ◽  
M. A. Quddus ◽  
R. B. Noland
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Dead Reckoning ◽  
Integrated System ◽  
Vehicle Performance ◽  
Performance And Emissions ◽  
System Data ◽  
Kalman Filter Algorithm ◽  
Filter Algorithms

This paper describes the features of an extended Kalman filter algorithm designed to support the navigational function of a real-time vehicle performance and emissions monitoring system currently under development. The Kalman filter is used to process global positioning system (GPS) data enhanced with dead reckoning (DR) in an integrated mode, to provide continuous positioning in built-up areas. The dynamic model and filter algorithms are discussed in detail, followed by the findings based on computer simulations and a limited field trial carried out in the Greater London area. The results demonstrate that use of the extended Kalman filter algorithm enables the integrated system employing GPS and low cost DR devices to meet the required navigation performance of the device under development.

scholarly journals ADDRESSING ISSUES IN INERTIAL NAVIGATION OF MICRO AERIAL VEHICLES (MAV) VIA AN EXTENDED KALMAN FILTER WITH BIASED SENSORS

2012 ◽  
pp. 17-23
Author(s):  
SANKETH AILNENI
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Integrated System ◽  
Gps Measurements ◽  
Micro Electro Mechanical Systems ◽  
Fully Integrated ◽  
Aerial Vehicle ◽  
Update Rate

This paper presents two ways to implement Extended Kalman Filter (EKF) algorithm for Inertial Navigation of a Micro Aerial Vehicle (MAV). The objective is to develop a fully integrated system using Micro electro mechanical systems (MEMS) inertial sensors combined with low-update rate Global positioning system (GPS) measurements. The approach uses three accelerometers, three gyroscopes and GPS measurements to aid the EKF algorithm. Two ways to implement EKF (15-state and splitarchitecture) are presented and observability issues are addressed in each case. EKF performance was evaluated by comparing the estimates with the simulated truth data.

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