基于最大后验估计的动态星图目标提取方法

Star Extraction Method for Dynamical Star Image Based on Maximum A Posteriori Estimation

摘要:

深空探测时,由于星敏感器受到多种复合运动影响,使拍摄星图存在严重运动模糊,信噪比低。采用Sureshrink小波方法去噪,能够有效保留星图的边缘信息和灰度信息;根据星图梯度符合拉普拉斯分布的先验信息,提出一种基于最大后验概率估计方法的星图去模糊模型,并对动态模糊星图进行去模糊处理。仿真结果表明,高动态时,经过所提出的去模糊方法处理后的星图不会出现维纳滤波方法中的振铃影响,处理速度和提取质心精度优于Richardson-Lucy滤波,质心精度优于0.1个像素,满足动态星敏感器的处理精度和速度要求。

Abstract:

In deep space exploration, the star images obtained with the star sensor will suffer from severe motion blur due to multiple compound motions, and the signal-to-noise ratio is low. The Sureshrink wavelet method is proposed to denoise and it can effectively preserve the star image edge information and gray information. Meanwhile, a deconvolution model of star image based on the maximum posteriori estimation was proposed according to the gradient of star image obeying Laplace distribution, and then it was utilized to recover the blurred star images. Simulation results show that the extraction accuracy is dramatically improved after deblurring, the proposed method generates less ringing effect compared with Wiener filter and presents better performance in terms of extracting speed and accuracy compared with Richardson-Lucy filter, and the centroid accuracy is better than 0.1 pixels, which satisfies the accuracy and speed requirements of the dynamic star sensor.