Development of Advanced Extended Kalman Filter for Precise Estimation of GPS Receiver Position

AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.

Download Full-text

Extended Kalman Filter for GPS Receiver Position Estimation

Advances in Intelligent Systems and Computing - Intelligent Engineering Informatics ◽

10.1007/978-981-10-7566-7_47 ◽

2018 ◽

pp. 481-488 ◽

Cited By ~ 3

Author(s):

N. Ashok Kumar ◽

Ch. Suresh ◽

G. Sasibhushana Rao

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Position Estimation ◽

Gps Receiver

Download Full-text

Performance Evaluation by Measurement Combination of Loosely Coupled GPS/INS Integration

10.20944/preprints202012.0338.v1 ◽

2020 ◽

Author(s):

Daero Lee

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

System Integration ◽

Estimation Error ◽

Gps Receiver ◽

Loosely Coupled ◽

Filter Performance ◽

Air Vehicle ◽

Measurement Units ◽

Inertial Measurements

This paper presents an extended Kalman filter derivation for loosely coupled GPS (Global Positioning System)/INS (Inertial Navigation System) integration based on quaternion attitude representation using the Earth-Centered Earth (ECEF) Frame. In this loosely coupling integration, both the position and velocity estimates from GPS receiver are used as the measurements to extended Kalman filter, and then they are integrated with inertial measurements from inertial measurement units (IMU) to estimate the attitude, position and velocity of an air vehicle. The velocity estimates which have centimeter level estimation error from the GPS receiver are used to improve the filter performance. For attitude estimation, the global attitude parameterization is given by a quaternion and a multiplicative quaternion-error approach is used to guarantee a normalization constraint of quaternion in the filter. Simulation results are shown to obtain the estimation of the attitude, position, velocity, biases and scale factors and to evaluate the performance of the EKF with the measurement combination composed of the two different t

Download Full-text

A Constrained Total Extended Kalman Filter for Integrated Navigation

Journal of Navigation ◽

10.1017/s0373463318000012 ◽

2018 ◽

Vol 71 (4) ◽

pp. 971-988 ◽

Cited By ~ 8

Author(s):

Vahid Mahboub ◽

Dorsa Mohammadi

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Integrated Navigation ◽

Gps Receiver ◽

Quadratic Constraints ◽

Remote Sensors ◽

The North ◽

Global Positioning ◽

North Of Iran

In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected random observed quantities which may appear in a dynamic model. In particular, this paper will consider the case of vision-based navigation. This algorithm is equipped with quadratic constraints and makes use of condition equations. The paper will show that the refined data from different sensors including a Global Positioning System (GPS) receiver, an Inertial Navigation System (INS) and remote sensors can be fused into a Constrained Total Extended Kalman Filter (CTEKF) algorithm. The CTEKF algorithm is applied to a case study in the Guilan province in the north of Iran. The results show the efficiency of the proposed algorithm.

Download Full-text