摘要
船位推算是水下航行器自主导航定位的重要手段。当采用对流工作模式的多普勒计程仪进行船位推算时,测速精度受海流影响较大,由此引起的船位推算误差较大。针对此问题,提出一种基于机动目标"当前"统计模型的水下组合导航模式,通过增加加速度观测信息,采用加速度均值、方差自适应Kalman滤波算法实现线运动参数的估计和流速修正并进行了仿真实验验证。仿真结果表明,使用本方法能得到较为准确的流速估计值,相同条件下使用加速度信息辅助船位推算比纯船位推算的定位精度有较大提高,其精度优于1 n mile/h。
Dead-reckoning is an important method for underwater vechile navigation. When doppler velocity log is in water-track mode, the dead-reckoning error affected by current is large. To solve this problem, an underwater navigation pattern based on mode maneuvering target ＂current＂ statistical model was put forward. By adding acceleration observation information and adopting acceleration mean-variance adaptive Kalman filtering algorithms, the linear-motion parameters could be estimated, and the vehicle speed could be corrected by using the sea current estimation. The simulation results show that a comparatively accurate current estimation method is obtained, and the acceleration aided dead-reckoning has higher precision （〈 1 n mile/h） than that of pure dead-reckoning under the same conditions.
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2012年第4期450-454,共5页
Journal of Chinese Inertial Technology
基金
国家自然科学基金项目(51175082)
总装十二五预研项目(51309030105
51309040301)
关键词
水下导航
当前统计模型
多普勒计程仪
捷联惯性测量单元
船位推算
underwater navigation
current statistical model
doppler velocity log
strapdown inertialmeasurement unit
dead reckoning
