摘要
为了提高无人机控制系统在自控飞行状态下的稳定性,必须提升高度数据的可靠性。文中提出2种方法实现这一目的:首先,通过对比当前主流的几款高度传感器的性能,挑选出一款合适的高度计取代现有高度计,该高度计很好地克服了原有高度计零点漂移大、精度差等缺点;再者,提出了一种自适应的卡尔曼滤波算法来克服现有的滤波算法无法在线调整滤波参数的缺点,该算法引入了一个缩放因子来在线调整滤波参数。通过使用无人机在外场做实验,验证了新高度计的优越性和新算法的有效性。
To enhance the flight stability of unmanned aerial vehicle in auto mode,the height data＇s reliability must be improved. To achieve this goal,two different methods were proposed here. Firstly,one suitable altitude sensor,who was capable of eliminating the old sensor＇s weakness like big zero drift and poor precision,can be selected from many by comparing their performance. Secondly,a new self-adaption Kalman filtering algorithm was proposed to improve present situation that filtering parameters cannot be adapted on line.This method was realized by introducing a scale factor into the filtering process.By conducting experiments in the open air,the new sensor＇s advantage and the new algorithm＇s effectiveness were verified.
出处
《自动化与仪表》
北大核心
2014年第9期57-61,共5页
Automation & Instrumentation
基金
国家重点基础研究发展计划(973计划)资助(2014CB845301/2/3)
2013年度开放基金(1214)
国家自然科学基金资助项目(61174053)
华南理工大学中央高校基本科研业务费资助项目(2014ZP0021)
关键词
无人直升机
自适应卡尔曼滤波
气压计
高度测量
unmanned aerial vehicle （UAV）
self-adaption Kalman filtering algorithm
barometer
altitude measurement
