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论文:2016,Vol:34,Issue(2):333-337 |
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引用本文： |
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余璐, 程咏梅, 刘夏雷, 刘楠. 基于机器学习的SMN可靠性分析及量测误差建模[J]. 西北工业大学学报 |
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Yu Lu, Cheng Yongmei, Liu Xialei, Liu Nan. A Method for Reliability Analysis and Measurement Error Modeling Based on Machine Learning in Scene Matching Navigation (SMN)[J]. Northwestern polytechnical university |
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| 基于机器学习的SMN可靠性分析及量测误差建模 |
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| 余璐, 程咏梅, 刘夏雷, 刘楠 |
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| 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 |
| 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。 |
| 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
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| A Method for Reliability Analysis and Measurement Error Modeling Based on Machine Learning in Scene Matching Navigation (SMN) |
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| Yu Lu, Cheng Yongmei, Liu Xialei, Liu Nan |
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| Key Laboratory of Information Fusion Technology at Department of Automatic Control, Northwestern Polytechnical University, Xi'an 710129, China |
| Abstract: |
| 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 and 0.001 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. |
| Key words:
mean square error
pixels
reliability analysis
support vector machines(SVM)
models
measurement errors
probability statistics
machine learning
motion blur
scene matching probability
scene matching navigation(SMN)
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| 收稿日期: 2015-10-09
修回日期:
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| DOI: |
| 基金项目: 西安市科技计划项目(CXY1436(9))资助 |
| 通讯作者:
Email： |
| 作者简介: 余璐(1991-),女,西北工业大学博士研究生,主要从事景象匹配导航、机器学习研究。
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| 余璐 在本刊中的所有文章 |
| 程咏梅 在本刊中的所有文章 |
| 刘夏雷 在本刊中的所有文章 |
| 刘楠 在本刊中的所有文章 |
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