Sensor-based Target Location Technology in Intelligent Transport Systems

Abstract

This paper explores the critical role of sensor-based targeting in Intelligent Transport Systems (ITS). The rapid growth of vehicles put forward challenges to congestion and emissions, and most contraries’ departments use ITS to solve the problem. The key to ITS is positioning the traffic participants precisely to collect the necessary data for the system operation. This study provides a review of previous research on sensor-based target localization and its importance in ITS. It discusses various sensors suitable for ITS applications, detailing their functionality and contribution to system efficiency, accuracy, and safety. In addition, this paper designs an experimental case that provides a deeper analysis of these sensors. This experiment tries to use minimum mean squared error and Kalman filtering to reduce error. The results of the study show that methods similar to minimum mean squared error and Kalman filtering can reduce the error effectively. This research contributes to understanding the use of sensors in ITS, digging their potential to revolutionize modern transport systems.