一种超声测距的鲁棒自适应建模方法

A ROBUST ADAPTIVE MODELING METHOD FOR ULTRASONIC RANGE FINDER

分析了超声测距的工作原理及特点 ,提出了一种新型的鲁棒自适应建模方法 .首先 ,利用在线递进滤波技术 ,有效地剔除采集数据中可能存在的“野点”;然后针对移动机器人中超声测距的不确定性特点 ,在自适应最小二乘估计 (AL S)的基础上 ,结合模糊理论 ,实现了鲁棒自适应最小二乘 (RAL S)建模 .最后 ,给出了一种基于 x2检验的模型收敛性检验方法 .通过实验对比分析 ,验证了 RAL S具有很好的实用性和鲁棒性 。

In this paper, the problem of modeling ultrasonic range finder under uncertainty is described. The inherent uncertainty in the sensor demands a 'soft' and robust approach to modeling the problem. Based on adaptive least square (ALS) technology, a robust adaptive least square modeling (RALSM) method is presented. First, we take advantage of on line adaptive filtering technology to kick out the error data. Then apply with fuzzy technology; we develop a RALS model for ultrasonic ranger finder. Finally, based on error analysis, a evaluation method to the proposed model is also presented. Experiments demonstrate the RLAS model with high feasibility, robustness and high self adaptability as well.