共查询到16条相似文献,搜索用时 203 毫秒
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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。 相似文献
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基于数据融合技术的单基站混合定位算法 总被引:1,自引:0,他引:1
针对传统的多基站定位容易引起通话质量干扰问题,提出了在TD-SCDMA蜂窝系统中,仅采用单个基站定位的混合定位算法(HPSR,hybrid positioning scheme with RSS).在该算法中,设计了一种新的冗余RSS数据融合模型(RDFM,RSS data fusion model)以消除非视距(NLOS,non-line-of-sight)误差,提高定位精度,并从理论推导证明了此模型的有效性.给出了运用在实际中的定位流程图,并进行了仿真验证,仿真结果表明,HPSR同时提高了TOA/AOA算法的稳定度和精确度. 相似文献
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本文阐述的是一种针对室内超宽带系统(UWB)的时间差到达和角度到达(TDOA/AOA)的混合定位技术。由于非视距传播(NLOS)误差确定为此系统的主要误差原因,所以本文使用卡尔曼滤波器来甄别和消除非视距误差,从而减小在室内UWB环境下的NLOS的时间到达(TOA)误差。本文加入了一种AOA选择功能。最后针对使用TDOA和有选择的AOA的室内移动定位追踪系统本文提出了一种改进的扩展卡尔曼滤波器(EKF)。仿真结果显示本文提出的混合定位方案可以有效响应在UWB环境下的NLOS/LOS变化,并且提高了定位精度。 相似文献
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针对无线传感器网络在非视距(NLOS)环境下利用接收信号强度(RSS)定位存在精度不足的问题,提出了一种新的基于二阶锥规划(SOCP)的鲁棒性定位算法。在假定非视距偏差上界的基础上构建了对非视距偏差量具有鲁棒性的定位方程,从而抑制了非视距偏差的干扰;接着利用凸优化技术将鲁棒性的定位问题转化为二阶锥规划问题,达到精确估计的目的,进而提高定位精度;此外,将定位问题推广到未知发射功率的情况,提出了一个迭代SOCP的算法。仿真结果表明,所提出的算法有效地解决了非视距定位中存在的问题,且定位精度要优于以往的牛顿迭代法、UT法以及SOCP法。 相似文献
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一种在非视距环境中的TDOA/AOA混合定位方法 总被引:2,自引:0,他引:2
提出了一种在非视距环境中的到达时间差/到达角混合定位方法。该方法使用了两步卡尔曼滤波。先用卡尔曼滤波器对到达时间测量值进行预处理,以消除TOA测量值中的NLOS误差。再把经过预处理的TOA测量值输入到用卡尔曼滤波器实现的TDOA/AOA混合定位器中进行定位。实验证明,该方法的定位误差性能优于单纯的TDOA定位方法及静态定位方法中泰勒级数展开法的误差性能。 相似文献
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TOA/RSS混合信息室内可见光定位方法 总被引:2,自引:0,他引:2
为提高室内定位精度,提出一种基于混合到达时间/接收信号强度(TOA/RSS)信息的定位方法。针对室内可见光定位中存在的多径效应造成的定位非线性误差,引入前置无迹卡尔曼滤波的粒子滤波算法,将TOA信息与RSS信息相融合,达到修正非线性误差的目的。然后综合考虑接收端惯性传感参数,对接收端进行运动分析,提升估算坐标的精度。在长宽均为5 m、高度为3 m的室内进行定位仿真,在12 W发光二极管(LED)发射功率下,所提方法获得了平均定位误差为2.02 cm的定位精度。仿真结果证明,所提定位方法的定位性能总体优于指纹定位方法和三边定位的RSS定位方法,具有较强的鲁棒性和较低的定位延迟。 相似文献
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Zoran M. Saric Dragan D. Kukolj Nikola D. Teslic 《Circuits, Systems, and Signal Processing》2010,29(5):837-856
In this paper, we consider the problem of acoustic source localization in a wireless sensor network based on different measured
signal quantities, such as the received signal strength (RSS), the angle of arrival (AOA) and the time of arrival (TOA). For
each of these quantities, an appropriate weighted least squares criterion function is developed to be used for sound source
localization. The weights of each criterion function take into account the decrease in the signal-to-noise ratio (SNR) with
distance from the source. In addition, RSS localization algorithm proposed in this paper provides improvement of the localization
accuracy for low SNR. Finally, separate criterion functions for RSS, TOA and AOA are used together to obtain minimal localization
error and maximal reliability of the acoustic source localization. Simulation analysis confirms improved performance of the
proposed localization algorithm. 相似文献
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Yishuang Geng Jie He Kaveh Pahlavan 《International Journal of Wireless Information Networks》2013,20(4):306-317
In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging. 相似文献
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《Vehicular Technology, IEEE Transactions on》2009,58(3):1157-1169
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用卡尔曼滤波器消除TOA中NLOS误差的三种方法 总被引:13,自引:0,他引:13
提出了三种改进的用卡尔曼滤波器消除到达时间(Time of Arrival,TOA)测量值中非视距(Non-Line of sight,NLOS)误差的方法。这三种方法从不同角度考察TOA测量值中NLOS误差的特点,分别对卡尔曼滤波器的迭代过程进行改进,有效地消除了TOA测量值中NLOS误差的随机性和正向偏差。与传统的NLOS误差消除算法相比,这三种方法均可获得较小的估计误差,并可实现实时处理。 相似文献
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The problem of locating mobile sensors has received considerable attention, particularly in the field of wireless communications. It is well-known that the presence of non-line-of-sight (NLOS) errors in the geo-location problem leads to severe degradation in the localization performance. In this paper, we propose a robust Bayesian method to mitigate the NLOS errors in location estimation of a single moving sensor, whereby the localization is performed using time-of-arrival (TOA) measurements. This method is based on the Markov chain Monte Carlo (MCMC) approach. Numerical simulations results illustrate the promising results of our method in a mixed line-of-sight (LOS) and NLOS environment. 相似文献