NLOS环境下基于信号检测的单次与二次散射路径识别

谢少杰, 邓平

谢少杰, 邓平. NLOS环境下基于信号检测的单次与二次散射路径识别[J]. 信号处理, 2020, 36(5): 733-740. DOI: 10.16798/j.issn.1003-0530.2020.05.012
引用本文: 谢少杰, 邓平. NLOS环境下基于信号检测的单次与二次散射路径识别[J]. 信号处理, 2020, 36(5): 733-740. DOI: 10.16798/j.issn.1003-0530.2020.05.012
Xie Shaojie, Deng Ping. Single and Secondary Path Recognition Based on Signal Detection in Non-Line-of-Sight Environment[J]. JOURNAL OF SIGNAL PROCESSING, 2020, 36(5): 733-740. DOI: 10.16798/j.issn.1003-0530.2020.05.012
Citation: Xie Shaojie, Deng Ping. Single and Secondary Path Recognition Based on Signal Detection in Non-Line-of-Sight Environment[J]. JOURNAL OF SIGNAL PROCESSING, 2020, 36(5): 733-740. DOI: 10.16798/j.issn.1003-0530.2020.05.012

NLOS环境下基于信号检测的单次与二次散射路径识别

基金项目: 国家自然科学基金(61871332)
详细信息
  • 中图分类号: TN911.7

Single and Secondary Path Recognition Based on Signal Detection in Non-Line-of-Sight Environment

  • 摘要: 针对NLOS(Non-Line-of-Sight)传播环境中单次与多次散射路径的识别问题,本文基于单次散射圆环模型建立了一种二次散射圆环模型,对该模型的统计特征进行了分析。同时,基于信号统计检测理论建立了一种单次与二次散射路径检测模型,然后根据是否已知先验概率两种情况,分别采用广义似然比和奈曼皮尔逊准则来检测识别两种散射路径。仿真结果表明:本文提出的检测识别方法在NLOS环境下能有效识别两种散射路径,相对于LPMD(Line of Possible Mobile Device)算法具有更高的识别率以及更小的虚警概率和漏警概率,且时间开销小,具有一定的应用价值。
    Abstract: Aiming at the problem of identifying single and multiple scattering paths in the NLOS(Non-Line-of-Sight) propagation environments, in this paper a secondary scattering ring model based on single scattering ring model is proposed, and the statistical characteristics of this model are analyzed. At the same time, based on the theory of signal statistical detection, a single-shot and a second-shot scattering path detection model are established. Then, based on whether the prior probabilities are known, the generalized likelihood ratio and Neiman Pearson criterion are utilized to detect and identify the two kind of scattering paths. Simulation results show that the detection and identification method proposed in this paper can effectively identify two scattering paths under the NLOS environment. Compared with the LPMD (Line of Possible Mobile Device) algorithm, it has a higher recognition rate and a smaller false alarm probability and missed alarm probability, and the time overhead is small, it has certain application value.
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出版历程
  • 收稿日期:  2020-01-12
  • 修回日期:  2020-03-13
  • 发布日期:  2020-05-24

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