Aircraft trajectory prediction method based on long short-term memory network

Abstract

The invention relates to the fields of air combat environments, data processing, deep learning and the like, and provides a method for realizing aircraft trajectory prediction by using a long short-term memory (LSTM) network under an uncertain sensing condition. Therefore, the technical scheme adopted by the invention is as follows: the method for predicting the trajectory of the aircraft based on the long-short-term memory network comprises the following steps of: eliminating noise interference carried by a sensor feature vector by using Kalman filtering; data preprocessing including downsampling, invalid value elimination and missing value complementation is carried out on the directly obtained state parameters, in addition, in order to improve the calculation stability, data is subjected to normalization processing, and the value range of input data is included in the interval of [0, 1]; and an LSTM-based trajectory prediction model is created, input and output of the network is defined, and the network is supervised and trained. The invention is mainly applied to the prediction occasion of the flight path of the unmanned aerial vehicle.

Description

technical field [0001] The invention relates to the fields of air combat environment, data processing, deep learning, etc., and solves the problem of predicting the flight trajectory of an aircraft under uncertain perception conditions. Specifically, it relates to a method of aircraft trajectory prediction based on long short-term memory network. Background technique [0002] Under the current international environment, the strength of the air force is the embodiment of a country's overall combat strength. In the actual air combat environment, the pilot needs to grasp the flight status of the enemy aircraft in real time based on the real-time data information from the airborne sensors. If we can predict the future state information of the enemy aircraft in advance based on the existing sensor state parameter information, including possible future positions and possible flight trajectories, it will help us implement combat strategies such as interception, strike, and evasion...

AI Technical Summary

Problems solved by technology

For this reason, the technical scheme that the present invention takes is, based on the method for aircraft track prediction of long-term short-term memory network, utilizes Kalman filter to eliminate the noise interference that sensor feature vector has; Perform data preprocessing, including downsampling, invalid value elimination, and missing value complement. In addition, in order to improve calculation stability, the data is normalized, and the value range of the input data is included in the [0,1] interval; the construction is based on The trajectory prediction model of LSTM defines the input and output of the network, and supervises the training of the network

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