基于STM32的车载航位推算导航系统设计

发布时间：2018-08-20 07:50

【摘要】：随着城市交通道路系统的日益复杂,人们对车辆定位精度的要求也越来越高。全球定位系统(Global Position System简称GPS)是随着现代科学技术的迅速发展而建立的新一代精密卫星定位系统。GPS由于其全球性、全天候以及连续实时三维定位等特点,已经在军事和民用事业领域方面得到了广泛的发展。传统的车辆导航系统主要采用GPS技术对车辆进行定位。在车载的导航使用中,常常会因为遭遇到环境上的遮蔽因素而造成导航工作无法正常运作,从而定位精度大大降低。在高楼林立的巷道中,收信状况往往极差,当行进隧道中时,更是完全没有信号可用,这时可以运用航位推算技术(Dead Reckoning简称DR)作为暂时的导航工具。航位推算技术具有较强的独立性,同GPS定位技术形成了较强的互补,在短时间内,DR的正确性相当高,甚至可以高于GPS,但随着使用时间的增加,DR的误差累积效应会越来越大,导航的精确度就会大幅下降,这时必须回归到GPS系统来找出绝对的位置,才能再次使用DR。DR和GPS是相辅相成的车载导航系统,但目前商品化的产品仍然不多,主要的瓶颈在于DR传感器的准确度和成本,以及与导航系统整合的算法开发方面。本文采用航位推算技术对车辆导航系统进行研究,设计了一个基于STM32的车载导航定位系统。主要内容如下。 1.首先概括介绍了几种主要的导航系统以及其优缺点,又给出了国内外导航系统的发展现状,阐述了论文研究的意义。 2.针对车载航位推算导航系统,本文研究了该系统的硬件平台搭建和软件平台的设计,主要是对各个传感器数据的采集和处理。 3.航位推算技术是本论文的精髓所在,它可以通过自身集成的惯性传感器计算出汽车的速度和位置信息,从而在GPS信号出现失锁和多径效应时,提高定位精度。本文研究了由加速度计和陀螺仪组成的导航系统。 4.滤波是是抑制和防止干扰的一项重要措施,本文给出了卡尔曼滤波技术的理论算法,并做了大量的仿真。从仿真结果得知,卡尔曼滤波可以提高系统的精度,克服了纯惯导系统误差随时间累积的缺点。并对车载航位推算导航系统,进行了标准卡尔曼滤波算法的推导。 5.跑车测试,证明了车载导航定位系统采用航位推算技术的可靠性。
[Abstract]:With the increasing complexity of urban traffic road system, the requirement of vehicle positioning accuracy is becoming higher and higher. Global Positioning system (Global Position System) is a new generation of precise satellite positioning system, which is established with the rapid development of modern science and technology, because of its global, all-weather and continuous real-time three-dimensional positioning. There have been extensive developments in the military and civilian fields. The traditional vehicle navigation system mainly uses GPS technology to locate the vehicle. In the use of vehicle navigation, the navigation work is often unable to operate normally because of the shielding factors in the environment, thus the positioning accuracy is greatly reduced. In the roadway with tall buildings, the condition of receiving information is often very bad, and when the tunnel is moving, there is no signal available at all. At this time, (Dead Reckoning can be used as a temporary navigation tool. The dead-reckoning technique is independent and complementary to the GPS positioning technology. The accuracy of Dr is quite high in a short time, even higher than that of GPS, but with the increase of service time, the error accumulation effect of Dr will be more and more. The accuracy of navigation will drop dramatically, and you have to go back to the GPS system to find the absolute position before you can use DR.DR and GPS again as a complementary vehicle navigation system, but there are still not many commercialized products. The main bottleneck lies in the accuracy and cost of Dr sensors, as well as the algorithm development of integration with navigation system. In this paper, a vehicle navigation system based on STM32 is designed. The main contents are as follows. 1. At first, several main navigation systems and their advantages and disadvantages are introduced, then the development status of navigation systems at home and abroad is given, and the significance of the research in this paper is expounded. 2. In this paper, the hardware platform and software platform of the system are studied, which mainly collect and process the data of each sensor. The dead-reckoning technology is the essence of this paper. It can calculate the vehicle's speed and position information by the inertial sensor which can improve the positioning accuracy when the GPS signal appears the lost lock and multi-path effect. A navigation system composed of accelerometers and gyroscopes is studied in this paper. Filtering is an important measure to suppress and prevent interference. In this paper, a theoretical algorithm of Kalman filtering is presented, and a lot of simulations are done. The simulation results show that Kalman filter can improve the accuracy of the system and overcome the shortcomings of the pure inertial navigation system error accumulated with time. The standard Kalman filter algorithm for vehicle-mounted dead-reckoning navigation system is deduced. 5. The test of sports car proves the reliability of the navigation and positioning system using the technology of dead-reckoning.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U463.67;U495

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