The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.
|Original language||English (US)|
|Title of host publication||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - May 2020|
|Event||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of|
Duration: May 25 2020 → May 28 2020
|Name||IEEE Wireless Communications and Networking Conference, WCNC|
|Conference||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020|
|Country||Korea, Republic of|
|Period||5/25/20 → 5/28/20|
Bibliographical noteFunding Information:
This work was in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, Guandong Key R&D Project under Grant 2019B010153003, the open research fund of Key Laboratory of Wireless Sensor Network & Communication under Grant 2017003, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, and the National Science Foundation under Grants CNS-1932413 and CNS-1932139.