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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >A Fusion Framework Based on Sparse Gaussian–Wigner Prediction for Vehicle Localization Using GDOP of GPS Satellites
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A Fusion Framework Based on Sparse Gaussian–Wigner Prediction for Vehicle Localization Using GDOP of GPS Satellites

机译：GPS卫星GDOP的基于稀疏高斯-威格纳预测的车辆定位融合框架。

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摘要

In order to provide a robust estimate of vehicle position in all environments, especially, in challenging urban areas where GPS signals are blocked, a fusion framework based on sparse Gaussian-Wigner prediction (SG-WP) is proposed. This new approach combines the advantages of both the random matrix theory and the sparse property to provide enhanced vehicle localization capabilities. In this method, measurement noises are assumed to be non-Gaussian distributed, and a generalized error distribution is adopted as an approximation to non-Gaussian densities. To ensure the robustness and the stability of the proposed approach, road-test experiments in various scenarios, including free, partial, and complete GPS outages, were performed based on the geometric dilution of precision metric. During complete outages, the SG-WP fuses all available INS measurements to improve the vehicle position prediction, whereas in free outages, only GPS information is processed. Besides, information from both GPS and INS are taken as inputs during partial outages, and the slide window is then introduced to regulate the flow data. The experimental comparison with the existing prediction methods reveals that the proposed method can achieve accurate and reliable positioning for land vehicles in all considered environments when the measurement noises are Gaussian or non-Gaussian distributed.
机译：为了对所有环境中的车辆位置提供可靠的估计，特别是在GPS信号受阻的具有挑战性的城市地区，提出了一种基于稀疏高斯-维格纳预测（SG-WP）的融合框架。这种新方法结合了随机矩阵理论和稀疏属性的优点，以提供增强的车辆定位能力。在该方法中，假设测量噪声为非高斯分布，并且采用广义误差分布作为对非高斯密度的近似。为了确保所提出方法的鲁棒性和稳定性，基于精度度量的几何稀释度，在各种情况下（包括免费，部分和完全GPS中断）进行了路试实验。在完全停电期间，SG-WP融合所有可用的INS测量值以改善车辆位置预测，而在自由停电时，仅处理GPS信息。此外，在局部中断期间，将来自GPS和INS的信息作为输入，然后引入滑动窗口以调节流量数据。与现有预测方法的实验比较表明，当测量噪声为高斯分布或非高斯分布时，该方法可以在所有考虑的环境中实现对陆地车辆的准确，可靠的定位。

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