Augmenting GPS localization in vehicles

• Main Author: Tan, Hao
• Other Authors: Tay Wee Peng
• Format: Final Year Project
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/75499
• Institution: Nanyang Technological University

Positional information acquired through Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS), will increasingly become of an essential element of many modern vehicle-based services and applications as location-aware technologies revolutionize many aspects of commercial, public service and military sectors. A wide range of applications like the navigation and intelligent transportation services heavily relies on positional information with certain degree of accuracy. However, the inadequacies of GNSS like limited availability and accuracy in densely populated areas caused by line-of-sight occlusions and multipath obstructions, leads to divergence of positional estimates. As such, this problem became the primary impetus for Cooperative Localization (CL) method based on Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication devices with absolute observations from on-board resources such as GPS, Vehicular Sensors and Inertial Measurement Unit (IMU). This method utilizes the availability of distances between participating nodes like On-Board Units (OBUs) and Road-Side Units (RSUs) as main parameters while using radio ranging techniques such as the Received Signal Strength Indicator (RSSI) and Time of Arrival (TOA) and Dedicated Short-Ranged Communications (DSRC) Signals, which is the nominated medium for V2I and V2V communication. In this paper, the proposed method is to use sensor fusion of IMU, GNSS information and Dedicated Short-Ranged Communications (DSRC) signals to triangulate vehicle’s position through Matlab simulations. DSRC refers to the communication among V2I and V2V that is used to compute relative vehicle location, leading to highly accurate vehicle localization when integrated when GPS and IMU information. The Extended Kalman Filter (EKF) is a linearization technique that is based on the first order expansion of Taylor series of the non-linear measurement and state functions of the state model. It is the de facto standard in estimating cooperative localization system’s unknown state due to its low computational complexity and simplicity [1]. As such, the EKF was proposed as the linearization technique for processing TOA information to limit the undesirable effects of errors generated in the sensors and neighbouring vehicle’s position when computing the new locations. One section of the Pan-Island Expressway (PIE) is surveyed and modelled for simulation.