A GPS-aided inertial navigation system in direct configuration

Abstract

This work presents a practical method for estimating the full kinematic state of a vehicle, along with sensor error parameters, through the integration of inertial and GPS measurements. This kind of system for determining attitude and position of vehicles and craft (either manned or unmanned) is essential for real time, guidance and navigation tasks, as well as for mobile robot applications.The architecture of the system is based in an Extended Kalman filtering approach in direct configuration. In this case, the filter is explicitly derived from the kinematic model, as well as from the models of sensors error. The architecture has been designed in a manner that it permits to be easily modified, in order to be applied to vehicles with diverse dynamical behaviors.The estimated variables and parameters are: i) Attitude and bias-compensated rotational speed of the vehicle, ii) Position, velocity and bias-compensated acceleration of the vehicle and iii) bias of gyroscopes and accelerometers. Experimental results with real data show that the proposed method is enough robust for its use along with low-cost sensors.

Development of an IGVM Integrated Navigation System for Vehicular Lane-Level Guidance Services

“…8. The available measurement is the lateral & vertical velocity information in body frame based on the land vehicle dynamics with non-holonomic constraint (Shin 2001, Munguia 2014). The measurement model can be denoted as follows (Cho et al 2007): , ε y ).…”

Cascaded Kalman Filtering-Based Attitude and Gyro Bias Estimation With Efficient Compensation of External Accelerations

“…Recent attempts to address the gyro bias estimation have mostly relied on aid from other sensors such as global positioning system (GPS) [28], vision-aided systems [29] and map specific information [30]. Since, these methods use other expensive sensors and demand extra computational time which might be unsuitable in a low-cost battery powered environment.…”

Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

“…The force in this case is due to gravitational field of the earth, whereas the rotation in this case is due to rotation of the earth. This system consists of three main parts; the first part is gyroscope as the rotation sensor, the second part is accelerometer as the motion sensor and the third part is computer which is used to calculate the movement of vehicles [2]. Gyroscope and accelerometer observe continuously in position, velocity and attitude, in which the calculation is conducted in the specified reference frame with certain formula.…”

“…The Integration of the INS and GNSS have a such phenomenon which the INS sensor generates the position, velocity and attitude in faster rate than GNSS sensor. The INS sensor consists of triad accelerometer (for measuring acceleration rate), triad gyroscope (for measuring rotation rate) and a computer which is used to do calculation (Munguia, 2014). The calculation means integrate the position, velocity and attitude.…”