Research On Acoustic-based Indoor Localization Under NLOS Environment For Mobile Devices

Recent years,as the demand for indoor localization is increasing to support our daily life in large and complex indoor environments,sound-based localization technol-ogies have attracted researchers' attention because they have the advantages of being fully compatible with commercial off-the-shelf(COTS)smart mobile devices,they have high positioning accuracy and low-cost infrastructure.However,in the real indoor environment,there are many interference factors,such as people walking,furniture blocking,wall reflection and so on.The direct path between the transmitter and receiver is obstructed,the time-of-arrival of the signal will be delayed,which introduces dis-tance estimate a positive bias.This phenomenon is called Non-Line-of-Sight(NLOS).NLOS poses a great challenge to the localization algorithm which based on conven-tional Line-of-Sight(LOS)environment,and significantly degrade the positioning ac-curacy.In this paper,we design a localization algorithm based on acoustic signal channel statistics feature NLOS identification in the NLOS/LOS complex indoor environment.The main work and contributions of the paper include the following aspects:Firstly,by analyzing indoor acoustic propagations,the changes of acoustic channel from the LOS condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios.Then,an effi-cient approach to estimate relative channel gain and delay based on the cross-correla-tion method is proposed,which considers the mitigation of the Doppler Effect and re-duction of the computational complexity.And an adaptive threshold selection strategy based on SNR is proposed to discriminate the first arrival path and mitigate the influ-ence of multiple propagation.Secondly,novel features are extracted from these characteristics that capture the salient properties based on time delay characteristics,waveform characteristics,Rician K-factor and frequency characteristics of relative channel gain.Then,we realize acous-tic NLOS identification based on SVM classifiers.And the optimal kernel function and feature combination are chosen through a huge number of experiment data set.Finally,for static target,we propose a NLOS identification and discard based lo-calization strategy and a NLOS posterior probability based weighted least squares lo-calization strategy.For moving target,we propose a NLOS identification based Modi-fied Extended Kalman Filter(MEKF)and a NLOS posterior probability based MEKF to achieve the indoor NLOS/LOS mixed environment under the robust positioning tracking.