Improved vehicle navigation using sensor fusion of inertial, odometeric sensors with global positioning system. (16th July 2021)

• Record Type: Journal Article
• Authors: Anbu, N. Allan; Pinagapani, Arun Kumar; Mani, Geetha; Chandran, K.R.
• Is Part Of: International journal of vehicle autonomous systems. Volume 15:Number 3/4(2020)
• Page Start: 307
• Page End: 318
• Publication Date: 2021-07-16
• Subjects: GPS -- global positioning system -- INS -- inertial navigation system -- scaled transformation -- state estimation -- stochastic system -- unscented Kalman filter; Autonomous vehicles -- Periodicals; Motor vehicles -- Technological innovations -- Periodicals; Motor vehicles -- Electronic equipment -- Periodicals; Robots -- Control systems -- Periodicals; Intelligent transportation systems -- Periodicals; 629.20285
• Languages: English
• ISSNs: 1471-0226

Abstract

The primary objective of a navigation system is to continuously monitor the trajectory of a vehicle. Navigation for a land vehicle is implemented using different measurement systems, such as Inertial Navigation System (INS), radio navigation system, vision-based navigation and Global Positioning System (GPS). INS provides continuous information of position, velocity and attitude. However, its performance deteriorates with time since the errors tend to accumulate. GPS is an electromagnetic signal which is more accurate when compared to INS but cannot provide continuous and reliable position all the time. The drawbacks of these individual systems have given rise to the need for higher accuracy, integrity and robustness. This has led to the fusion of measurements from these sensors to obtain an improved performance in measuring the position, velocity of a vehicle. This paper discusses the simulation and implementation of an integrated navigation system using inertial, odometric sensors with GPS using scaled unscented Kalman filter. The method discussed involves obtaining a state transition model and a measurement model of the sensors and processing the states using scaled unscented Kalman filter to obtain better estimates of position and velocity.