针对超宽带(ultra wide band,UWB)定位中影响定位精度的非视距(non line of sight,NLoS)传播误差问题,提出了一种基于Kalman滤波的NLoS误差二次消除方法.该方法利用NLoS误差与测量误差之间的相互独立性,借助Kalman滤波将NLoS误差从总误...针对超宽带(ultra wide band,UWB)定位中影响定位精度的非视距(non line of sight,NLoS)传播误差问题,提出了一种基于Kalman滤波的NLoS误差二次消除方法.该方法利用NLoS误差与测量误差之间的相互独立性,借助Kalman滤波将NLoS误差从总误差中单独分离出来,对其进行实时估计,并将该NLoS误差估计值作为NLoS误差辨别及测距值修正的依据.通过Kalman滤波对到达时间(time of arrival,TOA)测距值进行二次估计、鉴别及修正以提高TOA测距精度,从而实现室内复杂环境下的UWB精准实时定位.仿真实验结果表明:该方法不仅能够对NLoS误差实现良好的跟踪估计,对视距(line of sight,LoS)/NLoS环境转变也具有较强的灵敏感知能力,同时NLoS误差测距值在应用该方法后的定位性能逼近于LoS环境下的理想状态.展开更多
超宽带(Ultra Wide Band, UWB)技术是一种新兴的无线载波通信技术,其具有发射信号功率谱密度低、系统复杂度低、定位精度高等优势,尤其适用于像电厂等密集多径场所的高速无线连接,但在传输过程中信号会被环境中的各种因素影响,进而会影...超宽带(Ultra Wide Band, UWB)技术是一种新兴的无线载波通信技术,其具有发射信号功率谱密度低、系统复杂度低、定位精度高等优势,尤其适用于像电厂等密集多径场所的高速无线连接,但在传输过程中信号会被环境中的各种因素影响,进而会影响室内定位的精度。基于此,针对室内非视距(Non Line of Sight, NLOS)环境下,提出一种非视距混合滤波加权算法,能够有效对测距数据进行平滑处理,进而降低异常值的影响,再利用时间差定位法(Time Difference of Arrival,TDOA)测量方法,在原始Chan算法的基础上提出一种改进的Chan定位算法,解决NLOS误差引起的定位信息不准确的问题,最终实现更精准的TDOA定位。仿真实验证明,所提算法在室内NLOS环境中具有更高的定位精度。展开更多
60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data...60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.展开更多
减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计...减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计特性加入到定位算法中,使用散射模型研究了3种定位算法,方差匹配算法,期望最大算法和贝叶斯算法。并对算法进行仿真,仿真结果表明,本算法性能优于传统定位算法。展开更多
文摘针对超宽带(ultra wide band,UWB)定位中影响定位精度的非视距(non line of sight,NLoS)传播误差问题,提出了一种基于Kalman滤波的NLoS误差二次消除方法.该方法利用NLoS误差与测量误差之间的相互独立性,借助Kalman滤波将NLoS误差从总误差中单独分离出来,对其进行实时估计,并将该NLoS误差估计值作为NLoS误差辨别及测距值修正的依据.通过Kalman滤波对到达时间(time of arrival,TOA)测距值进行二次估计、鉴别及修正以提高TOA测距精度,从而实现室内复杂环境下的UWB精准实时定位.仿真实验结果表明:该方法不仅能够对NLoS误差实现良好的跟踪估计,对视距(line of sight,LoS)/NLoS环境转变也具有较强的灵敏感知能力,同时NLoS误差测距值在应用该方法后的定位性能逼近于LoS环境下的理想状态.
文摘超宽带(Ultra Wide Band, UWB)技术是一种新兴的无线载波通信技术,其具有发射信号功率谱密度低、系统复杂度低、定位精度高等优势,尤其适用于像电厂等密集多径场所的高速无线连接,但在传输过程中信号会被环境中的各种因素影响,进而会影响室内定位的精度。基于此,针对室内非视距(Non Line of Sight, NLOS)环境下,提出一种非视距混合滤波加权算法,能够有效对测距数据进行平滑处理,进而降低异常值的影响,再利用时间差定位法(Time Difference of Arrival,TDOA)测量方法,在原始Chan算法的基础上提出一种改进的Chan定位算法,解决NLOS误差引起的定位信息不准确的问题,最终实现更精准的TDOA定位。仿真实验证明,所提算法在室内NLOS环境中具有更高的定位精度。
基金supported by National Natural Science Foundation of China(No.62101298)Collaborative Education Project between Industry and Academia,China(22050609312501)。
文摘60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.
文摘减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计特性加入到定位算法中,使用散射模型研究了3种定位算法,方差匹配算法,期望最大算法和贝叶斯算法。并对算法进行仿真,仿真结果表明,本算法性能优于传统定位算法。
