基于动态环境衰减的粒子滤波室内定位算法.docx

基于动态环境衰减的粒子滤波室内定位算法
摘要
室内定位在现代社会中具有广泛应用。在实现这一目标时，粒子滤波算法已被证明是一种有前途的方法。然而，在现实情况下，环境中的衰减导致粒子滤波的精度降低。针对这一问题，本文提出了一种基于动态环境衰减的粒子滤波室内定位算法。该算法通过考虑环境衰减的动态变化，通过预估随机游走模型和权重计算来提高粒子滤波的精度。
关键词：室内定位，粒子滤波，动态环境衰减。
Abstract
Indoorpositioninghaswideapplicationsinmodernsociety.Particlefilteringalgorithmhasprovedtobeapromisingmethodforachievingthisgoal.However,inpracticalsituations,attenuationintheenvironmentleadstoadecreaseintheaccuracyofparticlefiltering.Tosolvethisproblem,thispaperproposesaparticlefilteringindoorpositioningalgorithmbasedondynamicenvironmentalattenuation.Thealgorithmimprovestheaccuracyofparticlefilteringbyconsideringthedynamicchangesofenvironmentalattenuation,estimatingtherandomwalkmodel,andcalculatingtheweight.
Keywords:indoorpositioning,particlefiltering,dynamicenvironmentalattenuation.
Introduction
Indoorpositioningiswidelyusedinmodernsociety,suchasinhospitals,shoppingmalls,offices,andairports.Withthegrowingdemandforlocation-basedservices,indoorpositioningtechnologyhasbeendevelopedrapidlyinrecentyears.Theultimategoalofindoorpositioningistoprovideuserswithaccuratelocationinformation.However,itfacesnumerouschallenges,suchassignalinterference,multipatheffects,andnon-line-of-sight(NLOS)propagation.Tosolvetheseproblems,variousalgorithmshavebeenproposed,includingfingerprinting,triangulation,andparticlefiltering.
Amongthesealgorithms,particlefilteringhasshownexcellentperformanceinindoorpositioningduetoitsabilitytohandlenon-Gaussianandnon-linearproblems.However,inpracticalsituations,attenuationintheenvironmentleadstoadecreaseintheaccuracyofparticlefiltering.Theattenuationofthesignalvarieswiththeenvironment,whichleadstoadynamicchangeintheaccuracyofthepositioningalgorithm.Tosolvethisproblem,aparticlefilteringindoorpositioningalgorithmbasedondynamicenvironmentalattenuationisproposedinthispaper.
Methodology
Theproposedalgorithmconsistsofthreemainparts:prediction,weighting,andresampling.Thepredictionstageestimatesthestateofthesystemusingtherandomwalkmodel.Theweightingstagecalcu