Abstract：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.
[1] E Gonza'lez. Uncertainty Treatment in a Sureface Filling Mobile Robot. International Workshop RUR'95 Proceedings, 1995, 294-305 [2] G Oriolo. Real-Time Map Building and Navigation for Autonomous Robot in Unknow Environments. IEEE Tran. SMC. , 1998,28(3):316-332 [3] 罗本成, 原魁等. 机器人多路超声波环境探测器的研制. 中国科学院研究生院学报, 2002,19(2): 172-176 [4] R R Yager. A General Approach to the Fusion of Imprecise Information. International Journal of Intelligent Systems, 1997,12(1): 1-29 [5] 李圣怡, 吴学忠, 范大鹏. 多传感器融合理论及在智能制造系统中的应用. 长沙: 国防科技大学出版社, 1998, 119-195 [6] 徐科军. 传感器动态特性的实用研究方法. 合肥: 中国科技技术大学出版社, 1999 [7] 张树侠, 吴简彤. 数据建模及预报. 哈尔滨: 哈尔滨工程大学出版社, 1999 [8] 张贤达. 时间序列分析-高阶统计量方法. 北京: 清华大学出版社,1996 [9] 沈福民. 自适应信号处理. 西安: 西安电子科技大学出版社, 2001 [10] 褚健, 俞立等. 鲁棒控制理论及应用. 浙江: 浙江大学出版社, 2000 [11] 庄楚强, 吴亚森. 应用数里统计基础. 广州: 华南理工大学出版社, 1997 [12] Y M Zhang, Q M Zhu. Selecting Optimal ARMA Order by a Minimum Spectrum Distance Criterion. International Journal of System Science, 1999, 30(2): 165-174
