Recently, smart mobile devices, such as smartphone and tablet PC, have become so prevalent. Most of them are equipped with a set of sensors including a global positioning system (GPS) receiver, a digital magnetic compass, a gyroscope, and an ac-celerometer. Unlike traditional vehicle-fixed sensors, smartphone-embedded sensors can be utilized as a user-friendly and portable measurement probe for vehicle positioning systems, owing to their flexibility and mobility. However, GPS modules and inertial navigation system (INS) sensors, such as an accelerometer and a gyroscope, on smartphones consume a lot of battery power. Continued use of the battery for a long time may cause the battery to discharge immediately. Therefore, one of the main concerns for smartphone-based GPS/INS positioning algorithms is energy efficiency. Furthermore, low-cost INS sensors on smartphones may result in large localization errors due to sensor drift and bias. Unlike smartphone-based GPS/INS positioning algorithms, we use only the GPS receiver and digital compass without INS sensors. This makes it possible to offer more accurate positioning results and to save more energy. However, GPS receivers and digital compasses on smart-phones may continue to experience positional errors due to multi-path fading and disturbances in GPS signals and magnetic sources. Therefore, we propose an enhanced vehicle positioning method that provides more reliable localization results by fusing measurements from GPS receiver and digital compass based on a Bayesian filter, called a simplified Kalman filter (SKF). Compared to existing Bayes filters, such as Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), while the SKF is simple and intuitive to be implemented, it can achieve competitive positioning accuracy with less computational cost. Experimental results through various road configurations using the smartphone and test vehicle in real environments show that the SKF-based vehicle localization scheme can achieve about 92% higher energy-efficiency and about 31 % higher positioning accuracy than GPS/INS localization methods based on the KF, UKF, and PF.
- Kalman filter
- global positioning system (GPS)
- sensor fusion
- vehicle positioning
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications