| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系 === 89 === Due to the development of new technologies, the plane and missile fly much faster. Thus, the function of the radar must be enhanced. For an air-defense system, when the missile invades, the system should launch intercepting missiles to destroy the invading missile in midair. It is by no means an easy task, and demands the integration of satellite radar, ground radar, and intercepting missiles together with the computer. For a successful interception of the missile flying with such a high speed, the intercepting missiles must be equipped better Guidance Laws, and the radar should be able to predict target dynamics. We aim to develop an intelligent radar predictor to track the high-speed moving target precisely. Although a conventional Kalman filter can also predict target dynamics, it needs to know the statistics of the noise in the environment and the initial state in advance. To avoid the limitation of the Kalman filter, in this thesis, we propose using a neural-network approach for prediction. The feasibility of the proposed approach is demonstrated through simulations and its performance is compared with that of the conventional Kalman filter.
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