Bayesian approach to joint super-resolution and trajectory estimation for midcourse closely spaced objects via space-based infrared sensor

Liangkui Lin; Weidong Sheng; Dan Xu

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Bayesian approach to joint super-resolution and trajectory estimation for midcourse closely spaced objects via space-based infrared sensor

机译：Bayesian approach to joint super-resolution and trajectory estimation for midcourse closely spaced objects via space-based infrared sensor

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摘要

The problem of super-resolution and tracking for midcourse closely spaced objects (CSO) is examined using a space-based infrared sensor. Within a short time window, the midcourse CSO trajectories on the focal plane can be modeled as following a straight line with a constant velocity. Thus, the object's initial state (location and velocity on the focal plane) exclusively corresponds to its trajectory on the focal plane. Thereupon, the objects number, intensities and initial states, as well as the sensor noise variances, are considered random variables, and a Bayesian model is proposed which is utilized to define a posterior distribution on the joint parameter space. To maximize this distribution, reversible jump Markov chain Monte Carlo algorithm is adopted to perform the Bayesian computation. The proposed approach simultaneously used the multiple time-consecutive frame data to estimate model parameters. Compared with the single-frame method, it not only gains the super-resolution capability but also can directly estimate focal plane trajectories without using explicit data association techniques. Results show that the performance (estimation precision of objects number, focal plane locations, intensities and ballistic trajectories for the CSO, together with the computation load) of the proposed approach outperforms the conventional single-frame and multiframe approaches.

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来源
《Optical Engineering》 |2012年第11期|117003-1-117003-12|共12页
作者
Liangkui Lin; Weidong Sheng; Dan Xu;
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作者单位

National University of Defense Technology, School of Electronics Science and Engineering, Changsha 410073, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
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Bayesian approach to joint super-resolution and trajectory estimation for midcourse closely spaced objects via space-based infrared sensor

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