Research of maneuvering target prediction and tracking technology based on IMM algorithm

Abstract

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.