Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/11/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Abstract. A kinematic description of a star spot in the focal plane is presented for star sensors under dynamical conditions, which involves all necessary parameters such as the image motion, velocity, and attitude parameters of the vehicle. Stars at different locations of the focal plane correspond to the slightly different orientation and extent of motion blur, which characterize the space-variant point spread function. Finally, the image motion, the energy distribution, and centroid extraction are numerically investigated using the kinematic model under dynamic conditions. A centroid error of eight successive iterations <0.002 pixel is used as the termination criterion for the Richardson-Lucy deconvolution algorithm. The kinematic model of a star sensor is useful for evaluating the compensation algorithms of motion-blurred images.
Robust H∞ consensus control problems of linear swarm systems with parameter uncertainties and time-varying delays are investigated. In this literature, a linear consensus protocol for high-order discrete-time swarm systems is proposed. Firstly, the robust H∞ consensus control problem of discrete-time swarm systems is transformed into a robust H∞ control problem of a set of independent uncertain systems. Secondly, sufficient linear matrix inequality conditions for robust H∞ consensus analysis of discrete-time swarm systems are given by the stability theory, and a H∞ performance level γ is determined meanwhile. Thirdly, the convergence result is derived as a final consensus value of swarm systems. Finally, numerical examples are presented to demonstrate theoretical results.
The increasing demand for air travel is stressing the current Air Traffic Control (ATC). This is likely to cause both safety and performance degradation in the near future. In order to solve this problem, increasing the automation level of ATC is an important development direction. So flight behavior recognizing is becoming a key technique for ATC, for it is the basis of other function of ATC, such as landing scheduling, conflict detection, and so on. This paper introduced support vector machine (SVM) to solve flight behavior recognizing in terminal area, and designed multi-classification algorithm flow. The simulation results show that SVM is equal to this task.
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