检测前跟踪框架下利用蒙特卡洛算法的空间碎片跟踪策略研究（英文）
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摘要
在轨卫星或者空间碎片数量的增多,是对空间目标地基自动观测的一个挑战。尽管北美防空司令部编目管理了绝大多数直径超过10 cm的空间物体,但由于轨道摄动,空间目标的位置信息(基于6个轨道根数)依然非常重要,并需要定期更新。在过去的几十年里,配备电子传感器的现代地基光电望远镜已广泛用于天体测量领域。然而,这种设备的跟踪性能主要取决于空间目标的大小和亮度。这些目标所在的天文图像会有不同的背景;而且,在基于凝视模式的短曝光实时观测过程中,运动目标和背景恒星在不同的信噪比下显示为类似的点扩散函数,难以辨认。本研究是为了实现对非高斯和动态背景的高灵敏度检测和跟踪能力的提高,并具有简单的系统机制和出色的计算效率。为突破该限制,将重点放在利用状态估计技术对微小卫星和暗弱目标进行跟踪上。提出一种基于神经网络的自适应运行高斯平均算法,用以从恒星背景及干扰下提取运动的空间目标。该方法随后被集成到了一个检测前跟踪框架中。该框架利用基于蒙特卡洛的粒子滤波跟踪空间目标。三段来自亚太地基光学空间目标观测系统(APOSOS)图像序列被用来对该跟踪策略进行评估。实验结果表明,该方法能够达到满意的跟踪性能。
The growing number of miniaturized satellites or small-body space debris is a challenging problem for autonomous ground-based space object observation.Although most space objects larger than 10 cm in diameter have been catalogued by North American Aerospace Defense Command,the precise orbital information of each space object(based on six orbital parameters)remains important and should be maintained periodically due to orbital perturbations.In the past decades,modern ground-based Electro-optic telescopes equipped with electronic detectors have been widely used in astrometry engineering.The tracking performance of this equipment primarily depends on the size and brightness of the space target.Moreover,in the real-time observation procedure based on STARE tracking mode in a short exposure time,the space object and stellar background will similarly appear in the point-spread function with different levels of signal-to-noise ratio under the variable conditions of background interference,which is difficult to recognize.The aim of present work is to achieve high-sensitivity detection and improved tracking ability for nonGaussian and dynamic backgrounds with a simple mechanism and computational efficiency.To overcome this limitation,we emphasize robust tracking of small size satellite and faint object via a state estimation technique.We proposed a neural-network based adaptive running Gaussian average algorithm to extract a moving space object from the stellar background and its interference.The algorithm was integrated to a Track-before-Detect(TBD) framework which used Monte-Carlo based particle filter.The integrated algorithms were adopted to track the space object.Three sequential astronomical image datasets taken by the Asia-Pacific Ground-Based Optical Space Object Observation System(APOSOS) telescopes under different conditions were used to evaluate the tracking strategy.The results showed that the scheme achieved a satisfying tracking performance.
The growing number of miniaturized satellites or small-body space debris is a challenging problem for autonomous ground-based space object observation.Although most space objects larger than 10 cm in diameter have been catalogued by North American Aerospace Defense Command,the precise orbital information of each space object(based on six orbital parameters)remains important and should be maintained periodically due to orbital perturbations.In the past decades,modern ground-based Electro-optic telescopes equipped with electronic detectors have been widely used in astrometry engineering.The tracking performance of this equipment primarily depends on the size and brightness of the space target.Moreover,in the real-time observation procedure based on STARE tracking mode in a short exposure time,the space object and stellar background will similarly appear in the point-spread function with different levels of signal-to-noise ratio under the variable conditions of background interference,which is difficult to recognize.The aim of present work is to achieve high-sensitivity detection and improved tracking ability for nonGaussian and dynamic backgrounds with a simple mechanism and computational efficiency.To overcome this limitation,we emphasize robust tracking of small size satellite and faint object via a state estimation technique.We proposed a neural-network based adaptive running Gaussian average algorithm to extract a moving space object from the stellar background and its interference.The algorithm was integrated to a Track-before-Detect(TBD) framework which used Monte-Carlo based particle filter.The integrated algorithms were adopted to track the space object.Three sequential astronomical image datasets taken by the Asia-Pacific Ground-Based Optical Space Object Observation System(APOSOS) telescopes under different conditions were used to evaluate the tracking strategy.The results showed that the scheme achieved a satisfying tracking performance.
引文
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[2]SHIVITZ R,KENDRICK R,MASON J,et al.Space Object Tracking(SPOT)facility[C]//Proceedings of SPIE.2014.
[3]UETSUHARA M,IKOMA N.Faint debris detection by particle based track-before-detect method[C]//Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference.2014.
[4]DAO P,RAST R,SCHLAEGEL W,et al.Track-before-detect algorithm for faint moving objects based on random sampling and consensus[C]//Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference.2014.
[5]JOHNSTON L A,KRISHNAMURTHY V.Performance analysis of a dynamic programming track before detect algorithm[J].IEEE Transactions on Aerospace and Electronic Systems,2002,38(1):228-242.
[6]GARVANOV I,KABAKCHIEV C.Radar detection and track in presence of impulse interference by using the polar hough transform[C]//Proceedings of the European Microwave Association.2007:170-175.
[7]YANAGISAWA T,NAKAJIMA A,KADOTA K,et al.Automatic detection algorithm for small moving objects[J].Publications of the Astronomical Society of Japan,2005,57(2):399-408.
[8]TORTEEKA P,GAO P Q,SHEN M,et al.Space debris tracking based on fuzzy running Gaussian average adaptive particle filter track-before-detect algorithm[J].Research in Astronomy and Astrophysics,2017,17(2):51-62.
[9]SALMOND D J,BIRCH H.A particle filter for track-before-detect[C]//Proceedings of the American Control Conference.2001:3755-3760.
[10]PAPOULIS A.Probability,random variables,and stochastic processes[M].机械工业出版社,1965.
[11]于欢欢,高鹏骐,沈鸣,等.空间碎片激光测距探测能力分析[J].天文研究与技术,2016,13(4):416-421.
[12]ODA H,YANAGISAWA T,KUROSAKI H,et al.Optical observation,image-processing,and detection of space debris in geosynchronous Earth orbit[C]//Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference.2014.