Trainable Kalman Filter Based on Recurrent Neural Network and its Application in Aviation Surveillance

Abstract

The most commonly used algorithm for aviation surveillance is Kalman filter. The accuracy of Kalman filter is affected by the accuracy of its parameters. When the parameters may change with environment change, the accuracy of traditional linear Kalman filter will be affected, in severe cases, filtering divergence will occur. This paper proposes an aviation surveillance filtering model that treats Kalman filter as the kernel of Recurrent Neural Network, uses Back Propagation Neural Network to predict parameters of Kalman filter. Which let Kalman filter be trainable and have ability to estimate parameters dynamically. Moreover, actual radar measurement data is used for radar filtering experiment, and the experiment results show the feasibility of this model, and show that this model has better accuracy and adaptability than traditional Kalman filter.