In order to find a more accurate prediction method for the track of an aerial target, this paper aims to establish a track prediction model by the theory of grey system based some track data of the target.
Aimed at the purpose, the authors have modified a model GM(1,1) with corrected residual error had presented in the literature, but it modeling the data with initial condition by the first data x(1), it is better to use the new information for the prediction, this means that the authors can use the last date x(n) as the initial conditions to modeling the track of air target to build a new model.
A modified model GM(1,1) with corrected residual error and initial conditions data x(n) is presented in this paper. The example demonstrates that the model is good fit for the prediction of the target tracks, especially for the case of that difficult to get accurate and reliable data when the data are limited.
And the presented GM model can set up online to improve prediction precision. Further research is a need to do for actual engineering application although the example demonstrates that the model is good fit for the prediction of the target tracks as the tag pointed out. When the target is moving in a uniform velocity along a line, the GM prediction has a good accuracy due to the orderliness of the data.
As the example, here data are given in the case of variable accelerate for this is more fit the factual applications. The precision of GM(1,1) could be enhanced with the boundary condition at the terminus, and so much as the residual error revising could be omitted.
The modified model effectually improved the veracity of track prediction of an aerial target. It is shown that the grey forecasting model can be used in the prediction of an air target tracks at least. Combining some other techniques could gain some further results.
Shen, M., Xie, Y., Xue, X. and Tian, X. (2009), "Study on track prediction of air target based on grey system model", Kybernetes, Vol. 38 No. 3/4, pp. 468-473. https://doi.org/10.1108/03684920910944173Download as .RIS
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