A Data Fusion System for Aerial Surveillance Information Based on Neural Network
Yanjun Jiang, Jian Hua · 2019
Aerial surveillance information fusion is a critical step in air traffic control systems. The surveillance system employs fusion algorithms to process measurement data from surveillance source such as radar, reduce measurement error, predict the future position of the aircraft, and provide the supervisor with predicted data according to the time interval set by the system. The most widely used fusion algorithm in air traffic control systems is the Kalman filter algorithm. This algorithm has been applied for several decades in related fields, but it requires considerable manpower and time to adjusting parameters, so it's use-cost is relatively high. The Kalman filter algorithm needs to adjust the parameters of the algorithm in different scenarios according to the position-accurate calibration data, considering the sector of the radar, the distance between the aircraft and the surveillance source, and the terrain. The process is very similar to the training process of supervised learning. This paper attempts to use neural networks to fuse surveillance information in order to replace the complex manual tuning process. Firstly, the neural network has the ability to represent complex features of things. The appropriate number of network layers and neurons can characterize the distribution of measurement errors in surveillance data. Secondly, we could train the network by using sufficient surveillance data and accurate target data to automatically capture features. It reduces labor and time commitments, and reduce the use-cost of the algorithm. This paper analyzed the shortcomings of the traditional fusion algorithm, introduced the overall structure of the fusion system, focused on the application of the neural network algorithm in the system, and gave the fusion result data of the algorithm.