基于置信度的自适应Kalman滤波定位方法  

Self-Adaptive Kalman Filtering Algorithm Based on Confidence Level

作　　者：于飞[1] 李元朋[2] 李志恒[2] 罗其俊[1] 高庆吉[1] 

机构地区：[1]中国民航大学机器人研究所,天津300300 [2]东北电力大学自动化系,吉林吉林132022

出　　处：《中国民航大学学报》2010年第2期42-46,共5页Journal of Civil Aviation University of China

基　　金：中国民用航空局科技基金项目(MHRD0702);中国民航大学科技基金项目(08CAUC-E07)

摘　　要：针对室外自主移动机器人因感知信息缺失或异常波动造成的定位失败或偏差过大的问题,设计了一种基于置信度的自适应Kalman滤波定位方法。该方法根据置信距离和置信度函数计算局部滤波器的置信度,并将其进行加权运算后作为全局滤波器的自适应因子,以得到更为准确的位姿估计。采用D-S证据理论对全局位姿进行评价,并给出单一传感器失效时的组合方案。试验结果验证了方法的有效性和鲁棒性,能满足实际定位的需要。The problem of positioning failure or excessive deviation of outdoor autonomous mobile robot often appears due to missed perceptional information or abnormal fluctuations. A self-adaptive federated Kalman filtering method based on confidence level was given in this paper. This method firstly defines confidence distance and the confidence function of the sensor data consistency, according to which confidence level of local filter can be calculated. Finally, we carry out weighted computing for confidence level which can distribute an appropriate adaptive factor for global filter , therefore, a more accurate pose estimation is obtained. In order to ensure the safely operation of robot, D-S theory is introduced to evaluate the global pose and give some kind of portfolio programs when a sensor is failure. Experimental results prove that this method is effective and robust and it also can meet the needs of the actual location.

关 键 词：组合定位 置信度函数 自适应滤波 D—S证据理论 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]