Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

中文翻译：

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在全球导航卫星系统挑战的城市车辆环境中使用稀疏估计进行多径建模和缓解

在城市环境中使用基于全球导航卫星系统的车辆导航时，多径干扰一直是最困难的问题之一。在本文中，我们开发了一种利用稀疏估计理论的多径缓解算法，该算法提高了城市环境中的绝对定位精度。将多径偏差作为附加定位误差建立导航观测模型，该方法假设接收信号中被多径污染的全球导航卫星系统信号占少数，这使得未知偏差向量变得稀疏。我们研究了一种改进的弹性网络方法来估计稀疏多径偏置向量，并且可以通过减去估计的多径误差来校正全球导航卫星系统的测量值。通过分析和实验结果验证了所提出方法的定位性能。