Suppression of stray light based on energy information mining.

The star tracker plays a critical role in precision aerospace missions due to its high accuracy, absolute attitude output, and low power consumption. For an optical sensor, the problem of stray light is always an important research issue. A star energy information mining method for stray light suppression is proposed in this study. The gray-level co-occurrence matrix and k-nearest neighbor algorithm are adopted to identify the types of stray light that enter the optical system. Effective recognition of the stray light types is an important premise for the following steps. Then the parameters are optimized during background estimation. When star spots are extracted, the local differential encoding combined with Levenshtein distance filtering is conducted to eliminate the interference noise spots. The proposed algorithm can achieve accurate star spot extraction even when stray light exists in real night sky observation experiments.

[1]  Zheng You,et al.  Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions , 2014 .

[2]  D. Glenar,et al.  Search for a high‐altitude lunar dust exosphere using Clementine navigational star tracker measurements , 2014 .

[3]  Ting Sun,et al.  Novel approach to improve the attitude update rate of a star tracker. , 2018, Optics express.

[4]  Ting Sun,et al.  Optimization method for star tracker orientation in the sun-pointing mode , 2017 .

[5]  Fang Jiancheng,et al.  Installation Direction Analysis of Star Sensors by Hybrid Condition Number , 2009, IEEE Transactions on Instrumentation and Measurement.

[6]  Ting Sun,et al.  Effective star tracking method based on optical flow analysis for star trackers. , 2016, Applied optics.

[7]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  Carl Christian Liebe,et al.  Accuracy performance of star trackers - a tutorial , 2002 .

[9]  Peter N. Yianilos,et al.  Learning String-Edit Distance , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Guangjun Zhang,et al.  Modeling of intensified high dynamic star tracker. , 2017, Optics express.

[11]  James E. Potter,et al.  Optimum mixing of gyroscope and star tracker data. , 1968 .

[12]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[13]  Philip Bille,et al.  A survey on tree edit distance and related problems , 2005, Theor. Comput. Sci..

[14]  Fei Xing,et al.  A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images , 2018, Light: Science & Applications.

[15]  Bin Li,et al.  Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time. , 2014, Optics express.

[16]  Fei Xing,et al.  Motion-blurred star acquisition method of the star tracker under high dynamic conditions. , 2013, Optics express.

[17]  William Robson Schwartz,et al.  Multi-scale gray level co-occurrence matrices for texture description , 2013, Neurocomputing.

[18]  Fei Xing,et al.  An accuracy measurement method for star trackers based on direct astronomic observation , 2016, Scientific Reports.

[19]  Ting Sun,et al.  Optimization method of star tracker orientation for sun-synchronous orbit based on space light distribution. , 2017, Applied optics.

[20]  Karl Sims,et al.  Handwritten Character Classification Using Nearest Neighbor in Large Databases , 1994, IEEE Trans. Pattern Anal. Mach. Intell..