A method for reliability analysis and measurement error modeling based on machine learning in scene matching navigation (SMN)

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.