基于LCVAE-CNN的多任務(wù)室內(nèi)Wi-Fi指紋定位方法
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Multi-task indoor Wi-Fi fingerprinting positioning method basedonLCVAE-CNN
WuShixun，ZengXinrui?，XuKai，Lan Zhangli，ZhangMiao，Jin Yue （Schoolof Information Science&Engineering，Chongqing Jiaotong University，Chongqing 40074，China）
Abstract：Indor Wi-Fireceivedsignal strengthindicator（RSI）fingerprintingwidelysupportslocation-basedservices. However，itfaceschallengessuchasthediffcultyofdatacolectionandsevereRSIfluctuationscausedbydynamicenvironmentalchanges，which hinderachieving high-acuracylocalization.Toimprove localizationaccuracyunderdatascarcityand dynamic environments，thispaper proposedadual-encoder structurethatindependentlyprocessedRSIdataandlocationcoor dinates.Thestudyintroducedageographicinformationlossfunctionandconstructedalocationconditionalvariationalautoencoder（LCVAE）model togenerate fingerprint data with geographicaccuracy，enhancing the localization model’sperformance.Aditionally，theresearchdesignedasharedconvolutional neuralnetwork（CNN）feature extractionlayer，ntegrating bothclasificationandregressionfunctions，andpresentedamultitask indoor Wi-Fifingerprintpositioning methodbasedon LCVAE-CNN.Experimental resultsshow that theproposed LCVAE-CNN methodachieves aflorclasification accuracyof 98.80% and a mean positioning error（MPE） of 6.79 meters on the UJIIndoorLoc dataset，and 97.22% and 5.44 meters respectivelyonthe Tamperedataset.Comparedtofiveexisting methods，theapproach improvesfloorclassificationaccuracyby atleast 1.9 percentage points and reduces MPE by a minimum of 19% ：
Key words：indoor positioning；LCVAE；CNN；data augmentation
0 引言
基于位置的服務(wù)（LBS）在導(dǎo)航、物流、游戲、人員跟蹤等領(lǐng)域發(fā)揮著重要作用。（剩余17365字）