基于数组信号子空间的自定位确定,适用于多路径环境下
Zhongkang Cao1, Pan Li1, Wanghao Tang1
1College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了一种新的阵列信号子空间配合方法,可以使用信号数据准确估计车辆位置,即使在具有挑战性的多路径环境中. 该方法有效地抑制非视线错误,提高定位精度和减少计算负载.
科学领域:
- 信号处理 信号处理
- 导航系统 导航系统
- 阵列信号处理 阵列信号处理
背景情况:
- 车辆定位传统上依赖于卫星导航,但在多路径环境中面临挑战.
- 现有的阵列自我定位确定方法由于多路径干扰而容易发生故障.
研究的目的:
- 为强大的车辆定位提出一个阵列信号子空间安装方法.
- 为了抑制信号接收中的多路径效应和非视线 (NLOS) 组件.
- 为了提高定位准确性和减少计算复杂性.
主要方法:
- 使用增强的空间平滑和根多个信号分类 (Root-MUSIC) 估计信号发生角度.
- 使用K-means集群来区分非视线 (NLOS) 信号与多路径组件.
- 使用带有 P 矩阵的信号子空间拟合 (SSF) 函数来减轻 NLOS 错误并缩小搜索区域.
主要成果:
- 与C矩阵,斜投影,ISF,MUSIC和SSF相比,提出的方法表明NLOS组件的抑制优越.
- 实现了至少7.64dB的计算复杂度降低.
- 在多路径条件下,估计精度比聚类算法提高3.10dB,比MUSIC,ISF和SSF提高11.77dB.
结论:
- 开发的阵列信号子空间安装方法为多路径环境中的车辆定位提供了增强的性能.
- 整合K-means集群和SSF显著提高了准确性和效率.
- 这种方法为卫星导航提供了一种可靠的替代方案,用于精确定位车辆.
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