Extended-image spatial tracking technique for deep-space optical downlinks

Haiping Tsou; Caroline S. Racho; Tsun-Yee Yan

doi:10.1117/12.363565

27 September 1999 Extended-image spatial tracking technique for deep-space optical downlinks

Haiping Tsou, Caroline S. Racho, Tsun-Yee Yan

Proceedings Volume 3762, Adaptive Optics Systems and Technology; (1999) https://doi.org/10.1117/12.363565
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

This paper describes an extended-image tracking technique based on the maximum likelihood estimation. The target image is assumed to have a known profile covering more than one element of a focal plane detector array. It is assumed that the relative position between the imager and the target is changing with time and the received target image has each of its pixels disturbed by an independent additive white Gaussian nose. When a rotation-invariant movement between imager and target is considered, the maximum likelihood based image tracking technique described in this paper is a closed-loop structure capable of providing iterative update of the movement estimate by calculating the loop feedback signals from a weighted correlation between the currently received target image and the previously estimated reference image in the transform domain. The movement estimate is then used to direct the imager to closely follow the moving target. This image tracking technique has many potential applications, including free-space optical communications and astronomy where accurate and stabilized optical pointing is essential.

Citation Download Citation

Haiping Tsou, Caroline S. Racho, and Tsun-Yee Yan "Extended-image spatial tracking technique for deep-space optical downlinks", Proc. SPIE 3762, Adaptive Optics Systems and Technology, (27 September 1999); https://doi.org/10.1117/12.363565

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KEYWORDS

Optical tracking

Signal to noise ratio

Space telescopes

Detector arrays

Telescopes

Motion estimation

Detection and tracking algorithms

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