Integration of microelectromechanical system-based inertial navigation system (MEMS-INS) and global positioning system (GPS) is a promising approach to vehicle localization. However, such a scheme may have poor performance during GPS outages and is less robust to measurement noises in changeable urban environments. In this article, we give an improved extended Kalman filter (IEKF) using an adaptation mechanism to eliminate the influence of noises in MEMS-INS and mitigate dependence on the process model. Especially, to guarantee accurate position estimation of the INS, a deep learning framework with multiple long short-term memory (multi-LSTM) modules is proposed to predict the increment of the vehicle position based on Gaussian mixture model (GMM) and Kullback–Leibler (KL) distance. The IEKF and the multi-LSTM are then combined together to optimize vehicle positioning accuracy during GPS outages in changeable urban environments. Numerical simulations and real-world experiments have demonstrated the effectiveness of the combined IEKF and multi-LSTM method, with the root-mean-square error (RMSE) of predicted position reduced by up to 93.9%. Or specifically, the RMSEs during GPS outages with durations 30, 60, and 120 s are 2.34, 2.69, and 3.08 m, respectively, which obviously outperform the existing method.

中文翻译：

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使用扩展卡尔曼滤波器和深度学习在 GPS 中断期间进行车辆定位

基于微机电系统的惯性导航系统 (MEMS-INS) 和全球定位系统 (GPS) 的集成是一种很有前途的车辆定位方法。然而，这样的方案在 GPS 中断期间可能性能不佳，并且对多变的城市环境中的测量噪声的鲁棒性较差。在本文中，我们提供了一种改进的扩展卡尔曼滤波器 (IEKF)，它使用自适应机制来消除 MEMS-INS 中噪声的影响并减轻对过程模型的依赖。特别是，为了保证 INS 的准确位置估计，提出了一种具有多个长短期记忆（multi-LSTM）模块的深度学习框架，以基于高斯混合模型（GMM）和 Kullback-Leibler 预测车辆位置的增量(KL) 距离。然后将 IEKF 和多 LSTM 结合在一起，以在多变的城市环境中的 GPS 中断期间优化车辆定位精度。数值模拟和实际实验证明了 IEKF 和多 LSTM 组合方法的有效性，预测位置的均方根误差 (RMSE) 降低了高达 93.9%。或者具体来说，GPS 中断期间持续时间为 30、60 和 120 s 的 RMSE 分别为 2.34、2.69 和 3.08 m，明显优于现有方法。