Bayesian Filtering And The Application In Tracking Of Antenna Deploying

Space deployable antenna has been widely used with the development of Aerospace Industry, the study on the space deployable antenna has also become a hot topic. In order to analyze the deploying accuracy, the Hoop-rib deployable antenna is built in ADAMS as the object of study, and the deploying process of it is tracked in this paper.Firstly, this paper introduces Bayesian filtering theory, studies on the Kalman Filter and Particle Filter of the specific realization of Bayesian Estimation based on state space model, simulates filtering algorithms and analyzes their properties. Kalman Filter can get the optimal estimation for the linear and Gaussian system in the Minimum Mean Square Error sense. For the nonlinear and Gaussian system, the Extended Kalman Filter (EKF) linearises all nonlinear models so that the traditional linear Kalman Filter can be applied. While Unscented Kalman Filter (UKF) realizes Kalman Filter by Unscented Translation (UT). For the nonlinear and non-Gaussian system. Particle Filter based on Monte Carlo Simulation realizes the recursion Bayesian filtering.Secondly, this paper discusses the widely used maneuvering models of target tracking, including constant velocity (CV) model, constant acceleration (CA) model, Singer model and current statistical (CS) model. As a result of variability of motion characteristics during deploying process of the Hoop-rib deployable antenna, this paper studies advantages of Interacting Multiple Model (IMM) algorithm in target tracking problems and demonstrates the performances though simulation.Finally, according to structure and motion characteristics of the Hoop-rib deployable antenna, deploying process is tracked based on single model and interacting multiple model and the tracking performances is analyzed.This paper realizes the dynamic tracking of the deployment process of the Hoop-rib deployable antenna though Bayesian filtering method, which presents a reference for engineering application.