基于机器学习的SMN可靠性分析及量测误差建模  

A Method for Reliability Analysis and Measurement Error Modeling Based on Machine Learning in Scene Matching Navigation( SMN)

作　　者：余璐[1] 程咏梅[1] 刘夏雷[1] 刘楠[1] 

机构地区：[1]西北工业大学自动化学院信息融合技术重点实验室,陕西西安710129

出　　处：《西北工业大学学报》2016年第2期333-337,共5页Journal of Northwestern Polytechnical University

基　　金：西安市科技计划项目(CXY1436(9))资助

摘　　要：针对复杂环境下景象匹配导航匹配概率不易实时统计以及量测误差统计特性不确定,提出基于机器学习的景象匹配可靠性分析及量测误差建模方法。首先建立基于机器学习的匹配概率及误差统计特性建模算法框架;然后以速高比变化带来的运动模糊为分析对象,选取支持向量机作为机器学习方法,定义匹配特征指标以及运动模糊下的匹配概率,给出景象匹配量测误差统计分析方法,并通过假设检验方法对景象匹配量测误差进行零均值检验;进一步在google earth制备的大样本数据库下完成匹配性能统计分析,以运动模糊、匹配得到的平均最高峰和平均峰值比作为支持向量机输入,统计得出的匹配概率和误差参数,即均值及方差作为支持向量机输出,通过训练得到匹配概率和景象匹配量测误差参数预测模型;最后根据该模型预测实时图的匹配概率和景象匹配量测误差参数,分析统计了不同模糊大小下实时图的匹配概率和景象匹配量测误差参数预测精度,结果表明:运动模糊小于40个像素时,阈值为5个像素和10个像素时匹配概率预测值与统计值的均方误差分别小于0.004和0.001,方差预测值与统计值的均方误差小于1个像素。In a complex environment,it is hard for scene matching probability to have real-time statistics,and the statistical property of measurement error is uncertain scene matching navigation. We propose a method for reliability analysis and measurement error modeling based on machine learning. Firstly,we propose the algorithm frame of reliability analysis and measurement error modeling based on machine learning. Secondly,we use the support vector machine（ SVM） as a tool of machine learning to study the aerial photography with motion blur brought about by velocity-height ratio. We define the characteristics indexes and scene matching probability under motion blurs and propose the measurement error statistics analysis method. The hypothesis testing is carried out to test whether the mean of scene matching measurement error is zero. Then through the statistical analysis of the scene matching performance in a large sample database generated by Google Earth,the motion blur calculated with velocity-height ratio,the mean ratios of highest peak and to highest peak obtained through scene matching are used as inputs of SVM. The scene matching probability model and measurement error parameters（ mean and variance） obtained with statistics are used as outputs of SVM. The scene matching probability and measurement error parameters are trained. Finally,we use the method to predict the scene matching probability of real-time image and the measurement error parameters under motion blur,which are analyzed at different degrees of motion blur. The prediction results show that the root mean square error of the prediction values and the statistics of scene matching probability is less than 0.004 and0.001 at the threshold values of 5 pixels. The root variant square error is less than 1 pixel when the motion blur is less than 40 pixels.

关 键 词：景象匹配 运动模糊 支持向量机 匹配概率 量测误差建模 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]