RSS协助的Ray-tracing室内定位算法
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摘要
针对室内定位,当信号受到非视距(non-line-of-sight,NLOS)和多径传播的影响时,本文提出一种接收信号强度(Received Signal Strength,RSS)协助的Ray-tracing室内定位算法,改进已经提出的基于虚拟基站方法的信号到达时间(Time of Arrival,TOA)和信号到达角度(Direction of Arrival,DOA)室内无线信号Ray-tracing模型,利用信号RSS测量值优化算法,实现TOA、DOA和RSS协同定位,提高室内多径及非视距环境下,无线定位的精度,降低算法复杂度,提高算法处理信号多重散射的能力并降低了对基站的依赖性适用环境更为广泛。首先通过RSS得到信号源可能存在的位置,随后利用Ray-tracing原理并使用虚拟基站,将非视距路径定位问题转化为视距路径定位问题,利用TOA和DOA对直射、透射、反射和绕射情况进行分析建模,最后使用最小二乘法对可能的位置进行筛选,得到信号源的最终位置。仿真结果表明,本算法较改进前拥有更高的定位精度。
For indoor positioning,when the signal is affected by NLOS and multipath propagation,an RSS-assisted Raytracing indoor location algorithm is proposed. Improve the positioning accuracy of the TOA and DOA indoor wireless signal Ray-tracing model based on the virtual base station method,the RSS signal measurement optimization algorithm is used to achieve co-location of TOA,DOA and RSS,improve indoor multipath and NLOS environments,reduce the complexity of the algorithm,improve the ability of the algorithm to process multiple scattering signals and reduce the dependence on the base station. The application environment is more extensive. First obtain the possible location of the signal source via RSS,then use the Ray-tracing principle and use the virtual base station to convert the NLOS path location problem to the NLOS location problem,using TOA and DOA for direct,transmission,reflection and diffraction situation is analyzed and modeled. Finally,the possible locations are screened using the least square method to obtain the final location of the signal source. Simulation results show that the algorithm has higher positioning accuracy.
For indoor positioning,when the signal is affected by NLOS and multipath propagation,an RSS-assisted Raytracing indoor location algorithm is proposed. Improve the positioning accuracy of the TOA and DOA indoor wireless signal Ray-tracing model based on the virtual base station method,the RSS signal measurement optimization algorithm is used to achieve co-location of TOA,DOA and RSS,improve indoor multipath and NLOS environments,reduce the complexity of the algorithm,improve the ability of the algorithm to process multiple scattering signals and reduce the dependence on the base station. The application environment is more extensive. First obtain the possible location of the signal source via RSS,then use the Ray-tracing principle and use the virtual base station to convert the NLOS path location problem to the NLOS location problem,using TOA and DOA for direct,transmission,reflection and diffraction situation is analyzed and modeled. Finally,the possible locations are screened using the least square method to obtain the final location of the signal source. Simulation results show that the algorithm has higher positioning accuracy.
引文
[1]Rajalakshmi K, Goyal M. Location-Based Services:Current State of The Art and Future Prospects[M]∥Optical and Wireless Technologies. Springer,Singapore,2018:625-632.
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[6]陈艺灵. RSS及混合室内可见光定位算法研究[D].大连:大连海事大学,2016.Chen Yiling. RSS and hybrid algorithm for indoor visible light localization[D]. Dalian:Dalian Maritime University,2016.(in Chinese)
[7]Huang L H,Shr K T,Lin M H,et al. A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform[J].Journal of Signal Processing Systems,2016,84(3):309-323.
[8]杨铮,吴陈沭,刘云浩.位置计算:无线网络定位于可定位性[M].北京:清华大学出版社,2014.Yang Z,Wu C M,Liu Y H. Location-based computing:localization and localizability of wireless networks[M].Beijing:Tsinghua University Press,2014.(in Chinese)
[9]郭晨.基于RSSI的无线网络定位技术研究[D].南京:东南大学,2015.Guo Chen. Research on RSSI-BASED localization technology in wireless network[D]. Nanjing:Southeast University,2015.(in Chinese)
[10]Zhang D,Liu Y,Guo X,et al. On distinguishing the multiple radio paths in rss-based ranging[C]∥INFOCOM,2012Proceedings IEEE. IEEE,2012:2201-2209.
[11]Tai C S,Tan S Y,Seow C K. Robust non-line-of-sight localisation system in indoor environment[J]. Electronics Letters,2010,46(8):593-595.
[2]Choi M S,Jang B. An Accurate Fingerprinting based Indoor Positioning Algorithm[J]. International Journal of Applied Engineering Research,2017,12(1):86-90.
[3]刘德亮.室内环境下无线定位关键技术研究[D].天津:天津大学,2015.Liu Deliang. Research on wireless localization in indoor environment[D]. Tianjin:Tianjin University,2015.(in Chinese)
[4]Horiba M,Okamoto E,Shinohara T,et al. An improved NLOS detection scheme using stochastic characteristics for indoor localization[C]∥Information Networking(ICOIN),2015 International Conference on. IEEE,2015:478-482.
[5]Viswanathan S,Srinivasan S. Improved path loss prediction model for short range indoor positioning using bluetooth low energy[C]∥SENSORS,2015 IEEE. IEEE,2015:1-4.
[6]陈艺灵. RSS及混合室内可见光定位算法研究[D].大连:大连海事大学,2016.Chen Yiling. RSS and hybrid algorithm for indoor visible light localization[D]. Dalian:Dalian Maritime University,2016.(in Chinese)
[7]Huang L H,Shr K T,Lin M H,et al. A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform[J].Journal of Signal Processing Systems,2016,84(3):309-323.
[8]杨铮,吴陈沭,刘云浩.位置计算:无线网络定位于可定位性[M].北京:清华大学出版社,2014.Yang Z,Wu C M,Liu Y H. Location-based computing:localization and localizability of wireless networks[M].Beijing:Tsinghua University Press,2014.(in Chinese)
[9]郭晨.基于RSSI的无线网络定位技术研究[D].南京:东南大学,2015.Guo Chen. Research on RSSI-BASED localization technology in wireless network[D]. Nanjing:Southeast University,2015.(in Chinese)
[10]Zhang D,Liu Y,Guo X,et al. On distinguishing the multiple radio paths in rss-based ranging[C]∥INFOCOM,2012Proceedings IEEE. IEEE,2012:2201-2209.
[11]Tai C S,Tan S Y,Seow C K. Robust non-line-of-sight localisation system in indoor environment[J]. Electronics Letters,2010,46(8):593-595.