光学学报, 2013, 33 (5): 0512001, 网络出版: 2013-01-14
基于非下采样Contourlet变换和映射最小二乘支持向量机的高精度星点定位方法
Hyper Accuracy Star Location Algorithm Based on Nonsubsampled Contourlet Transform and Mapped Least Squares Support Vector Machine
光计算 光学测量 非下采样Contourlet变换 映射最小二乘支持向量机 星点定位 误差分析 optics in computing optical measurement nonsubsampled contourlet transform mapped least squares support vector machine star location error analysis
摘要
为解决巡天相机稳像控制精跟踪级系统高精度的光闭环问题，提出一种基于非下采样Contourlet变换(NSCT)去噪预处理和映射最小二乘支持向量机(MLSSVM)回归校正的星点定位方法。针对星图特点，采用自适应的基于NSCT的去噪方法来减小随机误差。从频域角度分析平方质心法系统误差产生的机理，得到其近似解析表达式；利用蒙特卡罗数值仿真的方法，用带有高斯径向基函数(RBF)核的映射MISSVM进行回归分析，得到星点质心的理想位置和系统误差的非线性函数关系，并用它进行系统误差的校正。仿真实验结果表明，提出的方法抗噪能力更强，星点定位精度提高1~2个数量级，具有更为优越的星点定位性能。
Abstract
In order to resolve the problem of light closed loop for the level of fine tracking system of survey camera high-precision image stabilization control, a star location method is proposed based on nonsubsampled contourlet transform (NSCT) and mapped least squares support vector machine (MLSSVM). Aiming at the characteristics of the star image, the image is denoised by adaptive NSCT. By analyzing the systematic errors of square centroid method in the frequency domain, its approximate analytic expression is obtained. By using Monte-Carlo numerical simulation method, regression analysis based on MLSSVM with radial basis function (RBF) kernel is proposed. The nonlinear function between the ideal star centroid location and the systematic errors is obtained, and is used to correct the systematic errors. Simulation results show that the proposed method is stronger in anti-noise performance and the star location accuracy is improved by 1 to 2 order of magnetude.
刘南南, 徐抒岩, 胡君, 王栋, 曹小涛. 基于非下采样Contourlet变换和映射最小二乘支持向量机的高精度星点定位方法[J]. 光学学报, 2013, 33(5): 0512001. Liu Nannan, Xu Shuyan, Hu Jun, Wang Dong, Cao Xiaotao. Hyper Accuracy Star Location Algorithm Based on Nonsubsampled Contourlet Transform and Mapped Least Squares Support Vector Machine[J]. Acta Optica Sinica, 2013, 33(5): 0512001.
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