Track cycle prediction method based on resampling

Abstract

The invention discloses a track cycle prediction method based on resampling, and mainly solves the problems that in track prediction in the prior art, the prediction duration is short, and the prediction error is too large due to the fact that the motion state of a target changes in the future. According to the implementation scheme, the method comprises the steps of simulating a historical track and a future track of a maneuvering target; filtering, resampling and normalizing the historical track data of the target in sequence; constructing a neural network model composed of a Bi-LSTM layer, a Dropout layer, a Dense layer and an activation layer, and training the neural network model by using the preprocessed track data; generating part of historical track data by using a loop strategy, and calculating the historical track data by using the trained neural network model parameters; and carrying out smooth filtering on a calculation result to obtain a final predicted track. According to the method, the prediction error is small, the prediction time is long, when the target motion state changes in the future, the accurate prediction track can still be obtained, and the method can be used for target tracking.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to a cyclic prediction method of a track, which can be used for target tracking. Background technique [0002] Target trajectory prediction technology is to accurately predict the future trajectory state information of the target, and it is one of the key technologies in the field of target tracking. [0003] As the aviation flight environment tends to become more complex, due to the influence of uncertain factors such as system errors, bad weather, and abnormal performance of the sensor itself, the sensor will not be able to continue to detect the target track information, which will affect flight safety. Therefore, it is necessary to have a certain track prediction ability to provide more complete data information for subsequent target tracking to ensure flight safety. [0004] At present, trajectory prediction algorithms are mainly divided into dynamic models based on flig...

AI Technical Summary

Problems solved by technology

This type of algorithm is more accurate than classical forecasting methods, but there is still the problem of short forecasting time

[0007] To sum up, the existing target trajectory prediction technology has the disadvantages of large error, single prediction model, and short prediction time, which leads to poor accuracy of predicted trajectory and affects flight safety.

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