| 一种新的基于LPMD的散射路径识别算法 |
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| 引用本文： | 邓平,芮洋,邓水发.一种新的基于LPMD的散射路径识别算法[J].西南交通大学学报,2018,53(1):182-188. |
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| 作者姓名： | 邓平 芮洋 邓水发 |
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| 作者单位： | 西南交通大学信息科学与技术学院; |
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| 摘 要： | NLOS（non-line-of-sight）非视距误差是地面无线定位的主要误差来源.为了从NLOS散射信号中识别出单次散射路径和多次散射路径，提出了一种新的基于LPMD（line-of-possible-mobile-device）目标可能位置线的识别算法.该算法以所有LPMD线的交点来计算初步参考点位置，并通过距离初步参考点较远的一部分交点对剩余较近的交点做负期望补偿，以修正参考点的位置，同时使用参考点到各LPMD线的直线距离来构造散射路径类型判决表达式，进行单/多次散射路径判决.在不同的仿真参数条件下，分别对普通场景和特殊场景进行了散射路径识别仿真实验，结果表明，当侦测站和目标点之间的距离达到4 km时，本文算法的虚警和漏警概率分别只有3%和9%，比原有基于LPMD算法的分别降低了21%和8%.
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| 关 键 词： | 非视距 散射路径识别 单次散射 多次散射 目标可能位置线 |
| 收稿时间： | 2016-01-27 |
Novel Scattering Path Recognition Algorithm Based on LPMD |
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| DENG Ping,RUI Yang,DENG Shuifa.Novel Scattering Path Recognition Algorithm Based on LPMD[J].Journal of Southwest Jiaotong University,2018,53(1):182-188. |
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| Authors: | DENG Ping RUI Yang DENG Shuifa |
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| Abstract: | Non-line-of-sight (NLOS) error is the major source of error in ground wireless localization. In order to recognize single-bound scattering path and multiple-bound scattering paths from all the NLOS scattering paths, we propose a novel recognition algorithm based on line-of-possible-mobile-device (LPMD). This algorithm first utilizes the cross points of all the LPMDs to calculate the coordinate of a reference point. The algorithm then uses part of the cross points far away from the reference point to obtain minus expectation compensation to revise the location of reference point, builds the judgement formula of scatter path type based on the distance between each LPMD and the reference point to make single/multiple bound path judgement. Under normal and special scenery, the simulation tests of scatter path judgement are performed under different parameter conditions; the simulation results show when the distance between the target and the detecting station reaches 4 km. A false alarm probability and a false dismissal probability of the proposed algorithm can be limited to 3%and 9%, respectively. Further, these probabilities can be decreased by about 21% and 8%, respectively, when compared with the original LPMD algorithm.. |
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