Indoor Positioning Error Analysis Using a Cooperative Multi-Technology Simultaneous Localization and Signal Mapping in a Vehicular Environment

This paper introduces an error positioning analysis making use of a proposed cooperative multi-technology localization technique that leveraging WiFi, Ultra WideBand (UWB), LIght Detection And Ranging (LIDAR) signals and an Extended Kalman Filter (EKF) is able to guarantee a more precise positioning estimation and a signal map reconstruction. The analysis of the positioning error aims to show how the quality of the used sensors can affect the values estimated by the filter. In particular, we have considered three different classes of WiFi receiver sensitivity for evaluating how the receiver quality can affect the error positioning estimation. Moreover, we have also varied the number of WiFi emitters giving an indication of the goodness of the proposed approach in respect with other techniques. The numerical simulations are carried out considering a system in which sensors with different quality of sensing are taken into account. The results obtained have shown the greater robustness of the proposed strategy compared to others based on multilateration, especially when dealing with low-quality sensors, that is receiver with bad sensitivity, and with different number of WiFi emitters inside the considered area. It emerges that the adopted approach leads to acceptable results even in the presence of unreliable sensor measurements.