陆地车辆导航算法在Cortex-M4嵌入式处理器上的实现

A. Hamdy, A. Ouda, A. Kamel, Y. Elhalwagy
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引用次数: 1

摘要

目前,解决无人地面或无人飞行器的定位问题变得越来越关键。这种车辆使用基于全球定位系统(GPS)的导航系统来解决这个问题。然而,该系统可能会受到干扰和卫星可见性的影响,特别是在恶劣的环境中。因此,有必要设计并实现一种基于惯性导航系统(INS)与GPS集成的集成系统,以提供一种有前景的解决方案,以保证定位方案在这些环境中的可用性。此外,INS/GPS集成系统克服了各系统单独面临的短期和长期精度问题。本研究的目的是在嵌入式系统上设计并实现一个INS/GPS集成系统。它通过两个主要步骤进行;首先设计了一种利用扩展卡尔曼滤波(EKF)将惯导系统和GPS数据融合在一起的积分算法。在此步骤之后,第二步是在Tiva C单片机上实现该系统。所有用于硬件实现的传感器都是低成本的传感器,并且可以作为商业产品在线使用。然后,利用基于低成本传感器的GPS/INS集成系统解决移动车辆的定位问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Land Vehicle Navigation Algorithm Implementation on Cortex-M4 Embedded Processor
nowadays, solving the positioning problem of unmanned ground or aerial vehicle became more and more pivotal. Such vehicles use a navigation system based on the Global Positioning System (GPS) to solve this issue. However, this system could suffer from jamming and satellites visibility especially in harsh environments. Thus, it is necessary to design and implement an integrated system based on the integration between Inertial Navigation System (INS) and GPS to provide a promising solution to guarantee the availability of the positioning solution in these environments. In addition, INS/GPS integration system overcomes the problems faced by each system individually in short and long term accuracy. The purpose of this research is to design and implement an INS/GPS integrated system on an embedded system. It is carried out through two main steps; the first one is the design of an integration algorithm to fuse the INS and the GPS data together using Extended Kalman Filter (EKF). After this step, the second one is the implementation of this system on a Tiva C microcontroller. All the sensors used in hardware implementation are low cost sensor and available on-line as a commercial product. Then, the positioning problem of moving vehicles can be solved by using GPS/INS integration system based on low cost sensors.
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