摘要 针对室内复杂环境下信道状态信息的动态性问题,本文提出了一种面向室内Wi-Fi/行人航迹推算(Pedestrian Dead Reckoning,PDR)融合定位的自适应鲁棒卡尔曼滤波方法.该方法利用自适应鲁棒卡尔曼滤波将Wi-Fi传播模型与PDR定位信息进行多重融合,推算用户的最优估计位置.同时,基于滤波反馈机制,通过融合定位结果对加权最小二乘法中的路径损耗指数和滤波模型中的观测协方差进行动态修正,保证Wi-Fi传播模型接近于真实室内环境.实验结果表明,该方法能够有效解决室内复杂环境下单一Wi-Fi定位精度低和PDR累积误差的问题,此外,路径损耗指数和观测协方差的实时修正可以提高融合定位系统的定位精度和稳定性.
Abstract:In response to the problem of dynamic channel state information in complex indoor environment,this paper proposes an adaptive and robust Kalman filter approach for indoor Wi-Fi/Pedestrian Dead Reckoning (PDR) fusion localization.This approach conducts the multiple location information fusion of Wi-Fi propagation model and PDR to infer the optimal location estimate of the user.At the same time,based on the filter feedback mechanism,the fusion localization result is used to dynamically modify the path loss exponent in weighted least square method as well as the observation covariance in filter model with the purpose of guaranteeing that the Wi-Fi propagation model is close to the real indoor environment.The experimental results indicate that the proposed method is capable of well solving the problems of low localization accuracy by using the Wi-Fi solely and accumulative error in PDR.Furthermore,the real-time modification of path loss exponent and observation covariance improves the stability of the proposed fusion localization system.
[1] ALDIN N B,ERCELEBI E,AYKAC M.Advanced boundary virtual reference algorithm for an indoor system using an active RFID interrogator and transponder[J].Analog Integrated Circuits and Signal Processing,2016,88(3):415-430.
[2] 肖竹,王勇超,田斌,等.超宽带定位研究与应用:回顾和展望[J].电子学报,2011,39(1):133-141. XIAO Zhu,WANG Yong-chao,TIAN Bin,et al.Development and prospect of ultra-wideband localization research and application[J].Acta Electronica Sinica,2011,39(1):133-141.(in Chinese)
[3] HONG K,LeeS K,Lee K.Performance improvement in zigbee-based home networks with coexisting WLANs[J].Pervasive and Mobile Computing,2015,19:156-166.
[4] SHEN L L,HUI W W S.Improved pedestrian dead-reckoning-based indoor positioning by RSSI-based heading correction[J].IEEE Sensors Journal,2016,16(21):7762-7773.
[5] 陈永光,李修和.基于信号强度的室内定位技术[J].电子学报,2004,32(9):1456-1458. CHEN Yong-guang,LI Xiu-he.Signal strength based indoor geolocation[J].Acta Electronica Sinica,2004,32(9):1456-1458.(in Chinese)
[6] EL-KAFRAWY K,YOUSSEF M,EL-KEYI A,et al.Propagation modeling for accurate indoor WLAN RSS-based localization[A].Proceedings of IEEE 72nd Vehicular Technology Conference Fall[C].Ottawa:IEEE,2010.1-5.
[7] GU Z,CHEN Z,ZHANG Y,et al.Reducing fingerprint collection for indoor localization[J].Computer Communications,2015,83:56-63.
[8] JUNG S,LEE C O,HAN D.Wi-Fi fingerprint-based approaches following log-distance path loss model for indoor positioning[A].Proceedings of IEEE MTT-S International Microwave Workshop Series on Intelligent Radio for Future Personal Terminals[C].Daejeon:IEEE Computer Society,2011.1-2.
[9] PAULA T,BERNARDOS A M,CASAR J R.Weighted least squares techniques for improved received signal strength based localization[J].Sensors,2011,11(9):8569-8592.
[10] BARSOCCHI P,LENZI S,CHESSA S,et al.A novel approach to indoor RSSI localization by automatic calibration of the wireless propagation model[A].Proceedings of IEEE 69th Vehicular Technology Conference[C].Barcelona:IEEE,2009.1-5.
[11] BERNARDOS A M,CASAR J R,TARRIO P.Real time calibration for RSS indoor positioning systems[A].Proceedings of International Conference on Indoor Positioning and Indoor Navigation[C].Zurich:IEEE Computer Society,2010.1-7.
[12] CAI S,LIAO W,LUO C,et al.CRIL:An efficient online adaptive indoor localization system[J].IEEE Transactions on Vehicular Technology,2017,66(5):4148-4160.
[13] CHANG G.Kalman filter with both adaptivity and robustness[J].Journal of Process Control,2014,24(3):81-87.
[14] YANG Y,FAN X,ZHUO Z,et al.Amended kalman filter for maneuvering target tracking[J].Chinese Journal of Electronics,2016,25(6):1166-1171.