Cooperative mmWave PHD-SLAM with Moving Scatterers

来自 : 发布时间：2024-12-27

Abstract: Using the multiple-model (MM) probability hypothesis density (PHD) filter,millimeter wave (mmWave) radio simultaneous localization and mapping (SLAM) invehicular scenarios is susceptible to movements of objects, in particularvehicles driving in parallel with the ego vehicle. We propose and evaluate twocountermeasures to track vehicle scatterers (VSs) in mmWave radio MM-PHD-SLAM.First, locally at each vehicle, we generate and treat the VS map PHD in thecontext of Bayesian recursion, and modify vehicle state correction with the VSmap PHD. Second, in the global map fusion process at the base station, weaverage the VS map PHD and upload it with self-vehicle posterior density,compute fusion weights, and prune the target with low Gaussian weight in thecontext of arithmetic average-based map fusion. From simulation results, theproposed cooperative mmWave radio MM-PHD-SLAM filter is shown to outperform theprevious filter in VS scenarios. Submission history From: Hyowon Kim [view email] [v1] Tue, 22 Jun 2021 03:14:14 UTC (7,126 KB)[v2]Wed, 23 Jun 2021 01:30:19 UTC (7,126 KB) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv\'s community? Learn more about arXivLabs and how to get involved. subscribe to arXiv mailingsClick here to subscribeSubscribe