首页|Findings from Jilin University in Intelligent Transport Systems Reported (Multisource-multitarget Cooperative Positioning Using Probability Hypothesis Density Filter In Internet of Vehicles)
Findings from Jilin University in Intelligent Transport Systems Reported (Multisource-multitarget Cooperative Positioning Using Probability Hypothesis Density Filter In Internet of Vehicles)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Investigators publish new report on Transportatio n - Intelligent Transport Systems. According tonews reporting out of Jilin, Peo ple’s Republic of China, by NewsRx editors, research stated, “Accuratepositioni ng of intelligent connected vehicle (ICV) is a key element for the development o f cooperativeintelligent transportation system. In vehicular networks, lots of state-related measurements, especially themutual measurements between ICVs, are shared.”Financial support for this research came from National Natural Science Foundatio n of China (NSFC).Our news journalists obtained a quote from the research from Jilin University, “ It is an advisable strategyto fuse these measurements for a more robust positio ning. In this context, an innovative framework,referred to as multisource-multi target cooperative positioning (MMCP) is presented. In MMCP, ICVs arelocal info rmation source, that upload both the states of ICVs estimated by on-board sensor s and therelative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs areselected as the global targets, and the r elative vectors are converted into global measurements. Then,the MMCP is modell ed into a multi-target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM-PHD-LC) filter to match andfuse the global measurements to further improve the estimati on of ICVs. The evaluation results showthat our GM-PHD-LC can provide 10 Hz pos itioning services in urban area, and significantly improve thepositioning accur acy compared to the standalone global navigation satellite system. This paper pr oposesa low complexity Gaussian mixture probability hypothesis density (GM-PHD-LC) filter.”
JilinPeople’s Republic of ChinaAsiaIntelligent Transport SystemsTransportationJilin University