In the Electro-Optical acquisition-tracking-pointing system (ATP), the optical components will be damaged with the several influencing factors. In this situation, the rate will increase sharply when the arrival of damage to some extent. As the complex processing techniques and long processing cycle of optical components, the damage will cause the great increase of the system development cost and cycle. Therefore, it is significant to detect the laser-induced damage in the ATP system. At present, the major research on the on-line damage detection technology of optical components is for the large optical system in the international. The relevant detection systems have complicated structures and many of components, and require enough installation space reserved, which do not apply for ATP system. To solve the problem mentioned before, This paper use a method based on machine vision to detect the damage on-line for the present ATP system. To start with, CCD and PC are used for image acquisition. Secondly, smoothing filters are used to restrain false damage points produced by noise. Then, with the shape feature included in the damage image, the OTSU Method which can define the best segmentation threshold automatically is used to achieve the goal to locate the damage regions. At last, we can supply some opinions for the lifetime of the optical components by analyzing the damage data, such as damage area, damage position. The method has the characteristics of few-detectors and simple-structures which can be installed without any changes of the original light path. With the method, experimental results show that it is stable and effective to achieve the goal of detecting the damage of optical components on-line in the ATP system.
Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms.
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