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首页> 外文期刊>The Journal of Navigation >Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers
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Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

机译：基于信道模式识别的GNSS接收器上下文感知自适应多径补偿

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摘要

The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.
机译：研究了使用基于多径参数的模式识别方法来识别多径环境和接收器运动（例如行人，车辆）的类型的可能性。这使接收器可以调整其跟踪策略并优化其跟踪参数，以减轻代码多径影响。该方法采用了具有改进高斯核的支持向量机（SVM）分类方法。在不同环境中从接收信号的相关样本中提取一组时间和频谱特征，以训练分类器。然后将后者用于基于随机梯度的自适应多径补偿和跟踪技术的结构中，以根据环境和接收机运动来调整信号跟踪参数。使用Galileo E1B / C信号进行仿真和实际数据测量，以评估所提出的环境识别方法的有效性，并评估所提出的基于上下文的接收机参数调整技术对多径环境中跟踪性能的影响。测试结果表明，提出的分类器的准确度在86％至92％之间，跟踪性能提高了约15％。

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