软海底强干扰环境下的集矿车自适应定位  

Adaptive localization of mining vehicles under intensive seabed interference

作　　者：朱洪前[1,2] 胡豁生[1,3] 桂卫华[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]中南林业科技大学物流学院,长沙410004 [3]艾塞克斯大学计算机科学及电子工程学院

出　　处：《高技术通讯》2009年第11期1159-1163,共5页Chinese High Technology Letters

基　　金：国务院大洋专项科技攻关资助项目(DY105-03-02-06);国家自然科学基金(60505018)资助项目

摘　　要：针对海底集矿车长基线声学定位受工作噪声干扰以及航位推算精度受打滑干扰等问题,基于附加打滑参数的履带车运动学模型和根据湖试数据对导航系统过程噪声与测量噪声的描述,并提出了一种自适应时滞扩展卡尔曼滤波方法。该方法利用新息序列实现噪声统计特性自适应,然后考虑测量数据时延带来的影响,通过卡尔曼滤波器将长基线定位信息与航位推算信息进行融合,得到集矿车的位置估计。仿真结果证实,该自适应卡尔曼滤波器能有效地适应过程噪声与测量噪声统计特性的变化,比常规卡尔曼滤波器具有更好的海底集矿车定位效果。Since long base line （LBL） based sonar localization systems of seabed mining vehicles are seriously affected by working environment noises, and their dead reckoning （DR） accuracy is seriously affected by vehicle slippage, this paper proposes an adaptive time delay Kalman filtering method based on a kinematic model for mining vehicles with sliding parameters and the description of the process and measurement noises based on the experiment data collected from a lake. The method uses the innovation sequence to achieve the adaptive statistics features of both the process noise and measurement noise, takes account of the influence of measurement data delay, and then obtains the localization estimate of a seabed mining vehicle through the fusion of the LBL data and the DR data by the Kalman filter. The simulation results prove that the adaptive Kalman filter can deal with the changing statistics features of process noise and measurement noise very well, and has the better localization estimation of the seabed mining vehicle than a normal Kalman Jilter.

关 键 词：海底 强干扰 集矿车 自适应定位 卡尔曼滤波 

分 类 号：TD424[矿业工程—矿山机电]