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An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications

机译：用于无缝INS / GPS集成陆地车辆导航应用的智能导航器

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摘要

The Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. However, it has been reported that KF-based techniques have certain limitations, which reflect on the position error accumulation during GPS signal outages. Therefore, this article exploits the idea of incorporating artificial neural networks to develop an alternative INS/GPS integration scheme, the intelligent navigator, for next generation land vehicle navigation and positioning applications. Real land vehicle test results demonstrated the capability of using stored navigation knowledge to provide real-time reliable positioning information for stand-alone INS-based navigation for up to 20 min with errors less than 16 m (as compared to 2.6 km in the case of the KF). For relatively short GPS outages, the KF was superior to the intelligent navigator for up to 30 s outages. In contrast, the intelligent navigator was superior to the KF when the length of GPS outages was extended to 90 s. The average improvement of the intelligent navigator reached 60% in the latter scenario. The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.
机译：卡尔曼滤波器（KF）已被实现为许多陆地车辆导航和定位应用程序的全球定位系统（GPS）和惯性导航系统（INS）的主要集成方案。但是，据报道，基于KF的技术有一定的局限性，这反映了GPS信号中断期间的位置误差累积。因此，本文利用结合人工神经网络的思想来开发用于下一代陆地车辆导航和定位应用的替代INS / GPS集成方案（智能导航器）。实际陆地车辆测试结果表明，可以使用存储的导航知识为独立的基于INS的导航提供长达20分钟的实时可靠的定位信息，而误差小于16 m（相比之下，误差为2.6 km KF）。对于相对较短的GPS中断，KF在长达30 s的中断时间内优于智能导航仪。相比之下，当GPS中断时间延长到90 s时，智能导航仪就优于KF。在后一种情况下，智能导航器的平均改进达到60％。本文提供的结果有力地表明了将智能导航器作为INS / GPS集成陆地车辆导航系统的核心算法的潜力。

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