Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities  

作　　者：Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed 

机构地区：[1]Faculty of Electrical Engineering,Swedish College of Engineering and Technology,Wah Cantt,Pakistan [2]Department of Electrical Engineering,Faculty of Electrical and Computer Engineering,University of Engineering and Technology,Peshawar,Pakistan [3]Department of Electrical Engineering,Remote Sensing Unit,Northern Border University,Arar,Saudi Arabia [4]Data Science and Artificial Intelligence Department‐College of Science and Information Technology,Irbid National University,Irbad,Jordan [5]Innovation Education and Research Center for On‐Device AI Software(Bk21),Department of Computer Science and Engineering,Chungnam National University,Daejeon,Republic of Korea [6]Department of Electrical and Communication Engineering,United Arab Emirates University(UAEU),Al Ain,United Arab Emirates

出　　处：《CAAI Transactions on Intelligence Technology》2023年第4期1140-1149,共10页智能技术学报（英文）

摘　　要：Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.

关 键 词：Cramér‐Rao lower bound greedy successive anchorization industrial internet of things LOCALIZATION 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]