From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments

Zhenyuan Zhang; Heng Qin; Darong Huang; Xin Fang; Mu Zhou; Shenghui Guo

IEEE/CAA Journal of Automatica Sinica

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Z. Zhang, H. Qin, D. Huang, X. Fang, M. Zhou, and S. Guo, “From singleton to collaboration: Robust 3D cooperative positioning for intelligent connected vehicles based on hybrid range-azimuth-elevation under zero-trust driving environments,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–18, Jan. 2025.

Citation:

Z. Zhang, H. Qin, D. Huang, X. Fang, M. Zhou, and S. Guo, “From singleton to collaboration: Robust 3D cooperative positioning for intelligent connected vehicles based on hybrid range-azimuth-elevation under zero-trust driving environments,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–18, Jan. 2025.

Citation:

Z. Zhang, H. Qin, D. Huang, X. Fang, M. Zhou, and S. Guo, “From singleton to collaboration: Robust 3D cooperative positioning for intelligent connected vehicles based on hybrid range-azimuth-elevation under zero-trust driving environments,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–18, Jan. 2025.

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From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments

Funds: This work was supported in part by the National Natural Science Foundation of China (62273065, 62003064, 62303386), the Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0014), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K201800701, KJQN202000717), and Sichuan Science and Technology Program (2024NSFSC0525)

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Author Bio:
Zhenyuan Zhang (Member, IEEE) received the master and Ph.D. degrees from Chongqing University of Posts and Telecommunications (CQUPT). He was a Joint-Cultivated Ph.D. Researcher with the Georgia Institute of Technology, USA. He joined Chongqing Jiaotong University (CQJTU), where he has been an Associate Professor with the School of Information Science and Engineering since 2019. His research interests include deep learning, wireless signal processing, and multi-agent distributed perception

Heng Qin received the B.S. degree from Chongqing Jiaotong University (CQJTU) in 2022. Now he is pursuing the master degree at the School of Information Science and Engineering, Chongqing Jiaotong University. His major research interests include collaborative sensing and positioning

Darong Huang (Member, IEEE) received the B.S. degree in applied mathematics from Hubei National Institute in 2000, the M.S. degree in applied mathematics from Liaoning University in 2003, and the Ph.D. degree in control theory and control engineering from Chongqing University in 2006. From 2011 to 2021, he was a Professor with the College of Information Science and Engineering, Chongqing Jiaotong University. Since 2022, he held also the position of Professor with the Engineering Research Center of Autonomous Unmanned System Technology of Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, and the School of Artificial Intelligence, Anhui University. His research interests include fault diagnosis and fault-tolerant control, transportation information, and control technology

Xin Fang (Member, IEEE) received the Ph.D. degree from University of Electronic Science and Technology of China (UESTC) in 2019. He is currently a Lecturer with the School of Mechanical and Electronic Engineering, Southwest Petroleum University (SWPU). His research interests include radar signal processing, weak target detection and tracking, and collaborative localization

Mu Zhou (Senior Member, IEEE) received the B.S., M.S., and Ph.D. degrees in information and communication engineering from Harbin Institute of Technology in 2006, 2008, and 2012, respectively. He was a Joint-cultivated Ph.D. candidate at the University of Pittsburgh, USA, and a Post-doctoral Research Fellow at the Hong Kong University of Science and Technology, China. Afterward, he joined Chongqing University of Posts and Telecommunications (CQUPT), where he has been a Full Professor since 2014. He is now the Associate Dean of Graduate School of CQUPT and the Vice Director of Mobile Communications Engineering Research Center, Ministry of Education. His research interests include quantum radar, wireless localization and sensing, and information fusion. Currently, he serves as the Associate Editor for the IEEE Transactions on Vehicular Technology, the Area Editor for the Physical Communication (Elsevier), the Editor for the EURASIP Journal on Wireless Communications and Networking (Springer), and the Early Career Advisory Board Member for the IEEE/CAA Journal of Automatica Sinica. He was listed in the World’s Top 2% Scientists published by Stanford University in 2023

Shenghui Guo (Member, IEEE) received the Ph.D. degree in control theory and control engineering from Tongji University in 2016. He was a Postdoctoral Fellow with the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics from 2017 to 2019. He is currently an Associate Professor with the School of Electronics and Information Engineering, Suzhou University of Science and Technology. His research interests include observer design, model-based fault detection, and fault-tolerant control
Corresponding author: Darong Huang, e-mail: drhuangjs@ahu.edu.cn
Received Date: 2024-04-01
Accepted Date: 2024-07-17

Available Online: 2024-10-14

Abstract

Abstract

Reliable and accurate cooperative positioning is vital to intelligent connected vehicles (ICVs), in which vehicle-vehicle relative measurements are integrated to provide stable location-aware services. However, in zero-trust autonomous driving environments, the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance. With this in mind, this paper presents an ultra-wide bandwidth (UWB) based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments. Firstly, to overcome measurement degradation under non-line-of-sight (NLOS) propagation conditions, this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter (DKF) by integrating relative range-azimuth-elevation measurements, unlike the state-of-the-art methods that rely on only one single relative range information to update motion states. More specifically, in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack (either false data injection (FDI) or denial of service (DoS)), we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks. To this end, a singular-value decomposition (SVD)-assisted decoupled DKF algorithm is proposed in this work, in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals. To verify the effectiveness in practical 3D NLOS scenarios, we design an intelligent connected multi-robot platform based on a robot operating system (ROS) and UWB technology. Consequently, extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks, especially in the case of hybrid FDI and DoS attacks. In addition, several critical discussions, including the impact of attack parameters, resilience assessment, and a comparison with event-triggered methods, are provided in this work. Moreover, a demo video has been uploaded in the supplementary materials for a detailed presentation.

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References(44)

References

[1]	C. Zu, C. Yang, J. Wang, W. Gao, D. Cao, and F.-Y. Wang, “Simulation and field testing of multiple vehicles collision avoidance algorithms,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045–1063, Jul. 2020. doi: 10.1109/JAS.2020.1003246
[2]	Y. Ma, Z. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, Mar. 2020. doi: 10.1109/JAS.2020.1003021
[3]	D. Huang, Y. Na, Y. Liu, Z. Zhang, and B. Mi, “Overview of cooperative fault-tolerant control driven by the full information chain of intelligent connected vehicle platoons under the zero-trust framework: Opportunities and challenges,” IEEE Intell. Transp. Syst. Mag., vol. 16, no. 1, pp. 22–39, Jan.–Feb. 2024. doi: 10.1109/MITS.2023.3319344
[4]	F. M. Ortiz, M. Sammarco, L. H. M. K. Costa, and M. Detyniecki, “Applications and services using vehicular exteroceptive sensors: A survey,” IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 949–969, Jan. 2023. doi: 10.1109/TIV.2022.3182218
[5]	H.-M. Cheng and D. Song, “Graph-based proprioceptive localization using a discrete heading-length feature sequence matching approach,” IEEE Trans. Robot., vol. 37, no. 4, pp. 1268–1281, Aug. 2021. doi: 10.1109/TRO.2020.3046419
[6]	R. Karlsson and F. Gustafsson, “The future of automotive localization algorithms: Available, reliable, and scalable localization: Anywhere and anytime,” IEEE Signal Process. Mag., vol. 34, no. 2, pp. 60–69, Mar. 2017. doi: 10.1109/MSP.2016.2637418
[7]	P. Yan, W. Wen, and L.-T. Hsu, “Integration of vehicle dynamic model and system identification model for extending the navigation service under sensor failures,” IEEE Trans. Intell. Veh., vol. 9, no. 1, pp. 2236–2248, Jan. 2024. doi: 10.1109/TIV.2023.3273185
[8]	G. Thandavarayan, M. Sepulcre, and J. Gozalvez, “Generation of cooperative perception messages for connected and automated vehicles,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 16336–16341, Dec. 2020. doi: 10.1109/TVT.2020.3036165
[9]	M. Rohani, D. Gingras, V. Vigneron, and D. Gruyer, “A new decentralized Bayesian approach for cooperative vehicle localization based on fusion of GPS and VANET based inter-vehicle distance measurement,” IEEE Intell. Transp. Syst. Mag., vol. 7, no. 2, pp. 85–95, Apr. 2015. doi: 10.1109/MITS.2015.2408171
[10]	N. Piperigkos, A. S. Lalos, K. Berberidis, and C. Anagnostopoulos, “Cooperative multi-modal localization in connected and autonomous vehicles,” in Proc. IEEE 3rd Connected and Automated Vehicles Symposium, Victoria, BC, Canada, 2020, pp. 1–5.
[11]	L. Yin, Q. Ni, and Z. Deng, “Intelligent multisensor cooperative localization under cooperative redundancy validation,” IEEE Trans. Cybern., vol. 51, no. 4, pp. 2188–2200, Apr. 2021. doi: 10.1109/TCYB.2019.2900312
[12]	A. K. Gostar, T. Rathnayake, R. Tennakoon, A. Bab-Hadiashar, G. Battistelli, L. Chisci, and R. Hoseinnezhad, “Centralized cooperative sensor fusion for dynamic sensor network with limited field-of-view via labeled multi-Bernoulli filter,” IEEE Trans. Signal Process., vol. 69, pp. 878–891, Dec. 2020.
[13]	Y. Wang, X. Wen, Y. Cao, C. Xu, and F. Gao, “Bearing-based relative localization for robotic swarm with partially mutual observations,” IEEE Robot. Automat. Lett., vol. 8, no. 4, pp. 2142–2149, Apr. 2023. doi: 10.1109/LRA.2023.3248378
[14]	F. Bounini, D. Gingras, H. Pollart, and D. Gruyer, “From simultaneous localization and mapping to collaborative localization for intelligent vehicles,” IEEE Intell. Transp. Syst. Mag., vol. 13, no. 4, pp. 196–216, Mar. 2020.
[15]	J. Xin, G. Xie, B. Yan, M. Shan, P. Li, and K. Gao, “Multimobile robot cooperative localization using ultrawideband sensor and GPU acceleration,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 4, pp. 2699–2710, Oct. 2022. doi: 10.1109/TASE.2021.3117949
[16]	K. Chen, G. Qi, Y. Li, and A. Sheng, “Cooperative localization and circumnavigation of multiple targets with bearing-only measurements,” J. Franklin Inst., vol. 360, no. 12, pp. 9159–9179, Aug. 2023. doi: 10.1016/j.jfranklin.2022.09.039
[17]	T. Wang, K. Hu, Z. Li, K. Lin, J. Wang, and Y. Shen, “A semi-supervised learning approach for UWB ranging error mitigation,” IEEE Wireless Commun. Lett., vol. 10, no. 3, pp. 688–691, Mar. 2021. doi: 10.1109/LWC.2020.3046531
[18]	A. G. Ferreira, D. Fernandes, S. Branco, A. P. Catarino, and J. L. Monteiro, “Feature selection for real-time NLOS identification and mitigation for Body-mounted UWB transceivers,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–10, Apr. 2021.
[19]	X. Jin, W. M. Haddad, and T. Yucelen, “An adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems,” IEEE Trans. Autom. Control, vol. 62, no. 11, pp. 6058–6064, Nov. 2017. doi: 10.1109/TAC.2017.2652127
[20]	W. Duo, M. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548
[21]	G. Franzè, W. Lucia, and F. Tedesco, “Resilient model predictive control for constrained cyber-physical systems subject to severe attacks on the communication channels,” IEEE Trans. Autom. Control, vol. 67, no. 4, pp. 1822–1836, Apr. 2022. doi: 10.1109/TAC.2021.3084237
[22]	J. Huang, Y. Tang, W. Yang, and F. Li, “Resilient consensus-based distributed filtering: Convergence analysis under stealthy attacks,” IEEE Trans. Ind. Inform., vol. 16, no. 7, pp. 4878–4888, Jul. 2020. doi: 10.1109/TII.2019.2960042
[23]	X. He, X. Ren, H. Sandberg, and K. H. Johansson, “How to secure distributed filters under sensor attacks,” IEEE Trans. Autom. Control, vol. 67, no. 6, pp. 2843–2856, Jun. 2022. doi: 10.1109/TAC.2021.3092603
[24]	Y. Shoukry and P. Tabuada, “Event-triggered state observers for sparse sensor noise/attacks,” IEEE Trans. Autom. Control, vol. 61, no. 8, pp. 2079–2091, Aug. 2016. doi: 10.1109/TAC.2015.2492159
[25]	Y. Liu and G.-H. Yang, “Event-triggered distributed state estimation for cyber-physical systems under DoS attacks,” IEEE Trans. Cybern., vol. 52, no. 5, pp. 3620–3631, May 2022. doi: 10.1109/TCYB.2020.3015507
[26]	T. K. Tasooji and H. J. Marquez, “A secure decentralized event-triggered cooperative localization in multi-robot systems under cyber attack,” IEEE Access, vol. 10, pp. 128101–128121, Dec. 2022. doi: 10.1109/ACCESS.2022.3227076
[27]	Y. Wang, Y. Yan, T. Shen, S. Bai, J. Hu, L. Xu, and G. Yin, “An event-triggered scheme for state estimation of preceding vehicles under connected vehicle environment,” IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 583–593, Jan. 2023. doi: 10.1109/TIV.2022.3181330
[28]	H. Lin, J. Lam, and Z. Wang, “Secure state estimation for systems under mixed cyber-attacks: Security and performance analysis,” Inf. Sci., vol. 546, pp. 943–960, Feb. 2021. doi: 10.1016/j.ins.2020.08.124
[29]	J. Strader, Y. Gu, J. N. Gross, M. De Petrillo, and J. Hardy, “Cooperative relative localization for moving UAVs with single link range measurements,” in Proc. IEEE/ION Position, Location and Navigation Symposium, Savannah, GA, USA, 2016, pp. 336–343.
[30]	A. Chakraborty, K. M. Brink, and R. Sharma, “Cooperative relative localization using range measurements without a priori information,” IEEE Access, vol. 8, pp. 205669–205684, Nov. 2020. doi: 10.1109/ACCESS.2020.3035470
[31]	K. Ansari, “Cooperative position prediction: beyond vehicle-to-vehicle relative positioning,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1121–1130, Mar. 2020. doi: 10.1109/TITS.2019.2902572
[32]	T. K. Tasooji, S. Khodadadi, and H. J. Marquez, “Event-based secure consensus control for multirobot systems with cooperative localization against DoS attacks,” IEEE/ASME Trans. Mechatron., vol. 29, no. 1, pp. 715–729, Feb. 2024. doi: 10.1109/TMECH.2023.3270819
[33]	Y. Ma, Z. Nie, S. Hu, Z. Li, R. Malekian, and M. Sotelo, “Fault detection filter and controller co-design for unmanned surface vehicles under DoS attacks,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1422–1434, Mar. 2021. doi: 10.1109/TITS.2020.2970472
[34]	X. Shan, A. Cabani, and H. Chafouk, “Cooperative localization based on GPS correction and EKF in urban environment,” in Proc. Int. Conf. Innovative Research in Applied Science, Engineering and Technology, Meknes, Morocco, 2022, pp. 1–8.
[35]	X. Tao, B. Zhu, S. Xuan, J. Zhao, H. Jiang, J. Du, and W. Deng, “A multi-sensor fusion positioning strategy for intelligent vehicles using global pose graph optimization,” IEEE Trans. Veh. Technol., vol. 71, no. 3, pp. 2614–2627, Mar. 2022. doi: 10.1109/TVT.2021.3139006
[36]	X. Ding, Z. Wang, and L. Zhang, “Event-triggered vehicle sideslip angle estimation based on low-cost sensors,” IEEE Trans. Ind. Inform., vol. 18, no. 7, pp. 4466–4476, Jul. 2022. doi: 10.1109/TII.2021.3118683
[37]	X. Xia, R. Xu, and J. Ma, “Secure cooperative localization for connected automated vehicles based on consensus,” IEEE Sensors J., vol. 23, no. 20, pp. 25061–25074, Oct. 2023. doi: 10.1109/JSEN.2023.3312610
[38]	L. Li, H. Yang, Y. Xia, and H. Yang, “Event-based distributed state estimation for linear systems under unknown input and false data injection attack,” Signal Process., vol. 170, pp. 1–14, May 2020.
[39]	W. Li, Y. Jia and J. Du, “Distributed kalman filter for cooperative localization with integrated measurements,” IEEE Trans. Aerosp. Electron. Syst., vol. 56, no. 4, pp. 3302–3310, Aug. 2020. doi: 10.1109/TAES.2019.2953372
[40]	T.-K. Chang, K. Chen, and A. Mehta, “Resilient and consistent multirobot cooperative localization with covariance intersection,” IEEE Trans. Robot., vol. 38, no. 1, pp. 197–208, Feb. 2022. doi: 10.1109/TRO.2021.3104965
[41]	C. D. Persis and P. Tesi, “Input-to-state stabilizing control under denial-of-service,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 2930–2944, Nov. 2015. doi: 10.1109/TAC.2015.2416924
[42]	H. Gao, H. Dong, Z. Wang, and F. Han, “Recursive minimum-variance filter design for state-saturated complex networks with uncertain coupling strengths subject to deception attacks,” IEEE Trans. Cybern., vol. 52, no. 10, pp. 11121–11132, Oct. 2022. doi: 10.1109/TCYB.2021.3067822
[43]	T. K. Tasooji and H. J. Marquez, “Event-triggered consensus control for multirobot systems with cooperative localization,” IEEE Trans. Ind. Electron., vol. 70, no. 6, pp. 5982–5993, Jun. 2023. doi: 10.1109/TIE.2022.3192673
[44]	J. Hu, Z. Wang, H. Gao, and L. K. Stergioulas, “Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements,” Autom., vol. 48, no. 9, pp. 2007–2015, Mar. 2012. doi: 10.1016/j.automatica.2012.03.027

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From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments

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