Real-Time Implementation of GPS Aided Low-Cost Strapdown Inertial Navigation System

Abstract

As autonomous navigation becomes considerable and important because of the increased demand to their usage and benefits, therefore reliability and integrity issues become definite, specially when being implemented with commercially low-cost sensors. The objective of this thesis is to both develop and implement in real-time an INS/GPS integrated navigation system using the loosely-coupled linear Kalman filter. The importance of the implemented algorithms are to function appropriately and accurately using low cost inertial sensors where the rapid drift in sensors output requires a reliance on external and available aiding source as Global Positioning System (GPS).This thesis describes the theoretical development and practical implementation in real-time of strapdown inertial navigation system (INS) using commercial of the shelf low cost inertial measurement unit (IMU) aided with the Global Positioning System (GPS). When describing the IMU as a "low cost", this term means that this unit is built using standard low grade accelerometers and gyros which cannot conduct self-alignment. Therefore the thesis provides a desirable calibration procedure where the requirements of a precisely controlled orientation of the IMU can be relaxed. To do so, the thesis describes in detail the design, construction, error modelling analysis and calibration approach of six-degree of freedom (6DOF) IMU. On the other hand, calibration of (IMU) unit is one of the most challenging issues in navigation field as it requires high accuracy measurements in order to maintain acceptable readings from sensors, and normally the cost of calibration platform often exceeds the cost of developing and constructing a MEMS sensor based IMU.This thesis mainly discusses the development of inertial mechanization equations and algorithms that provides position, velocity and attitude of the host platform. Then, the data provided by inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the used sensors and the mathematical modelling of INS and the aiding sensors errors, however iii when fusing the INS data with GPS data, the errors can be bounded and the accuracy will increase. This thesis provides, both in practical and theoretical terms, the fusion processes adopted and real time implementation required for a high integrity aided inertial navigation system using state-of-art technology microcontroller MPC555 as the navigation computer. The theoretical work is verified by set of real-time experiments using our developed INS/GPS equipment -The developed IMU, standard GPS and MPC555-, the results have been compared with a top-notch INS/GPS Navigation device. The experimental results using our designed INS/GPS has shown that position and velocity accuracy can be archived using algorithms presented in this thesis work.iv

Low-Cost, Non-centralized, Vehicle Collision Prevention System

“…Using accelerometers and gyroscopes to find velocity and position is explained in [1][2][3][4][5]. The accelerometer gives the vehicle specific force in three directions in the body frame.…”

“…Using the initial attitude of the vehicle measured by the digital compass and the position measured by the GPS receiver, the three-axes acceleration and angular velocity measurements are integrated to obtain the position and velocity of the vehicle at 50 Hz [1][2][3][4][5]. The position and velocity along with ID of the vehicle is then transmitted to neighboring vehicles using a Zigbee transceiver [6,7].…”

Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

“…However, gimbaled systems are usually very expensive to be used commercially. [11][12][13][14][15]. The position, velocity, and attitude obtained for a vehicle would then be transmitted to other vehicles within a close range using the Zigbee network, so that the driver can be warned if there is a dangerous situation [20][21][22].…”

“…The position, velocity, and attitude obtained for a vehicle would then be transmitted to other vehicles within a close range using the Zigbee network, so that the driver can be warned if there is a dangerous situation [20][21][22]. Two sensor fusion algorithms are used in this thesis, which are: extended Kalman filter (EKF) [11][12] and extended information filter (EIF) [12]. When a filter is described as extended, it means that its state matrix is linearized.…”

MEMS Based SINS/OD Filter for Land Vehicles’ Applications

“…The recent advances in sensors technologies and sensor fusion algorithms contributed to the realization of highly integrated low-cost navigation systems. In most systems, GPS signal and the inertial measurement unit (IMU), which consists of three-axe accelerometers and three-axe rate gyros, are fused to estimate the position and velocity of the vehicle [1,2]. Nevertheless, the SINS/GPS is not a complete selfcontained system since the GPS signal may be disturbed or unavailable under some circumstances.…”