基于车辆运动模型辅助的智能手机平台车载组合导航算法

发布时间：2018-02-24 13:27

  本文关键词： 车载导航 车辆运动模型 组合导航 卡尔曼滤波　出处：《中国惯性技术学报》2017年02期 　论文类型：期刊论文

【摘要】：随着智能手机硬件性能的提升以及MEMS传感器技术的发展和应用普及,现有中高端智能手机平台中大多安装有消费级的微惯性测量单元和GPS接收机模块,将这两者相互结合,利用一定的信息融合方法,即可实现连续的车载导航定位。在智能手机平台中所采用的消费级MEMS惯性传感器模块其精度很低,通常难以满足车载DR导航算法的性能需求;同时在车辆行驶过程中,不可避免会经过高架、隧道等路径,也会导致GPS信号受到遮挡和屏蔽从而无法定位。针对以上问题,设计了适用于智能手机平台的基于车辆运动模型辅助的车载DR/GPS组合导航方案,推导建立了基于车辆侧向速度约束的组合导航算法模型,并以智能手机作为验证平台,测试验证了所设计的算法在无GPS环境下,采用智能手机平台中的消费级MEMS惯性测量组件,仍然可以在短时间内维持较高的导航定位性能。
[Abstract]:With the improvement of smart phone hardware performance and the development and application of MEMS sensor technology, most of the existing high-end smart phone platforms have consumer level micro inertial measurement unit and GPS receiver module, which are combined with each other. Using certain information fusion method, we can realize continuous vehicle navigation and positioning. The consumptive MEMS inertial sensor module used in smart phone platform has very low precision, and it is usually difficult to meet the performance requirements of vehicle Dr navigation algorithm. At the same time, in the course of vehicle driving, it is inevitable to pass through the viaduct, tunnel and other paths, which will also cause the GPS signal to be blocked and shielded and thus unable to locate. A vehicle DR/GPS integrated navigation scheme based on vehicle motion model is designed for smart phone platform. The integrated navigation algorithm model based on vehicle lateral speed constraint is derived and established. The smart phone is used as the verification platform. The test results show that the proposed algorithm can maintain high navigation and positioning performance in a short period of time by using the consumptive MEMS inertial measurement module in the smartphone platform without GPS.
【作者单位】： 南京航空航天大学导航研究中心;
【基金】：国家自然科学基金项目(61533008,61374115,61533009,61673208) 江苏省“333工程”科研资助立项项目(BRA2016405) 留学人员择优资助项目(2016年) 江苏省六大人才高峰资助项目(2013-JY-013) 航空科学基金项目(20165552043) 江苏高校优势学科建设工程项目和江苏省“物联网与控制技术”重点实验室基金资助 中央高校基本科研业务费专项资金资助(NS2017016,NZ2016104) 江苏省普通高校研究生科研创新计划项目(KYLX15_0264)
【分类号】：U495
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本文编号：1530398