Multipath interference detection method for vector receivers based on joint multi-channel SQM metrics
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摘要: 矢量接收机利用各跟踪通道间信息共享达到通道间相互辅助的作用，因此相对于传统的标量接收机能在复杂环境下提供更好的性能. 然而在城市峡谷等复杂环境下，多径传输会严重限制矢量接收机的性能，若能及时发现多径并加以处理则能消除多径干扰的影响. 本文从多径对矢量接收机的影响机理出发，研究基于信号质量监测(SQM)的多径检测问题. 在分析现有的针对标量接收机信号SQM指标的基础上，结合矢量接收机通道耦合的特点，提出一种联合多通道SQM指标的矢量接收机多径检测方法. 最后，通过仿真实验验证了所提方法能够有效地检测与直达信号幅度比小于0.5、相对码延时在0.3~0.6码片的多径干扰.
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关键词:
- 全球导航卫星系统(GNSS) /
- 多径干扰 /
- 矢量跟踪 /
- 信号质量监测 /
- 多通道联合检测
Abstract: Vector receivers provide better performance in complex environments than traditional scalar receivers by sharing information between tracking channels to assist each other. However, multipath interference can severely limit the performance of vector receivers in complex environments such as urban canyons, and the effects of multipath can be eliminated if it is detected and dealt with in a timely manner. This paper researches multipath detection based on signal quality monitoring (SQM) from the perspective of the mechanism of multipath effects on vector receivers. Firstly, this paper proposes a multipath detection method for vector receivers with joint multi-channel SQM metrics, based on the analysis of existing SQM metrics for scalar receiver signals, combined with the characteristics of vector receiver channel coupling. Finally, this paper verifis through simulation experiments that the proposed method can effectively detect multipath with amplitude ratio less than 0.5 and the relative code delay in the range of 0.3–0.6 chips. -
表 1 常用的SQM指标
接收机类型 指标名称 指标计算表达式 均值 标量跟踪 Delta[3] $\displaystyle\frac{ { {I_{\rm{E}}} - {I_{\rm{L}}} } }{ { {I_{\rm{P}}} } }$ 0 Ratio[3] $\displaystyle\frac{ { {I_{\rm{E}}} + {I_{\rm{L}}} } }{ { {I_{\rm{P}}} } }$ 1.9 Double Delta[6] $\displaystyle\frac{ {({I_{ {{\rm{E}}_1} } } - {I_{ {{\rm{L}}_1} } }) - ({I_{ {{\rm{E}}_2} } } - {I_{ {{\rm{L}}_2} } })} }{ { {I_{\rm{P}}} } }$ 0 ELP[8] ${\arctan }\left( {\displaystyle\frac{ { {Q_{\rm{E} } } } }{ { {I_{\rm{E} } } } } } \right) - {\arctan }\left( {\displaystyle\frac{ { {Q_{\rm{L} } } } }{ { {I_{\rm{L} } } } } } \right)$ 0 Slope[15] $\displaystyle\frac{ {2 \cdot ({I_{ {{\rm{L}}_1} } } - {I_{ {{\rm{L}}_2} } })} }{ { {I_{\rm{P}}}({d_1} - {d_2})} }$ −1 矢量跟踪 文献[16] $\left\{ \begin{gathered} \frac{ { {I_{\rm{P}}} - {I_{\rm{L}}} } }{ { {I_{\rm{E}}} + {I_{\rm{L}}} } }{\text{ } }\left| { {I_{\rm{E}}} } \right| \geqslant \left| { {I_{\rm{L}}} } \right| \\ \frac{ { {I_{\rm{P}}} - {I_{\rm{E}}} } }{ { {I_{\rm{E}}} + {I_{\rm{L}}} } }{\text{ } }\left| { {I_{\rm{E}}} } \right| < \left| { {I_{\rm{L}}} } \right| \\ \end{gathered} \right.$ 0.5 
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