A Radar Filtering Model for Aerial Surveillance Base on Kalman Filter and Neural Network

摘要

Aerial surveillance information fusion is a key subsystem in air traffic control system. An excellent aerial surveillance information fusion algorithm can get more accurate position estimation on the aircraft. The commonly used algorithm for aerial surveillance information fusion is Kalman filter. The filtering accuracy of the Kalman filter algorithm is affected by accuracy of its parameters. When parameters are inaccurate, it may even cause the filter to diverge, so how to determine the parameters of Kalman filter is a key problem. This paper proposes a filtering model that integrates Back Propagation Neural Network, Generalized Regression Neural Network and Kalman filter. The parameters of Kalman filter are adjusted during filtering process dynamically by neural networks, so that the adaptability of traditional Kalman filter is enhanced. The actual radar measurement data is used for filtering experiments, and experimental results show effectiveness of this model.