Using current statistical model for maneuvering target tracking has a good effect. However, maneuvering frequency and ultimate acceleration are defaulted by human's experience. When the given parameters are not accordant with actual situation, the capacity of tracking maneuvering target will decline. In view of the problem that the tracking performance of model is dependent on the prior parameters, this paper puts forward an adaptive tracking algorithm based on truncation gauss probability model for target tracking. In this model, the maneuvering situation of targets is characterized by the distance function, the status yawp and filtering gain of model is adaptively adjusted by using the exponential adjustment function to modulate maneuvering frequency and ultimate acceleration, which can improve the matching degree between maneuvering target model and the actual movement of the target. According to the simulation results, the capacity of tracking maneuvering target is improved, comparing TGPNMKF with ACS and TGPMKF.