# ADIBAS-Volterra aircraft trajectory prediction model based on improved dynamic integration of beetle tentacle searching

## TL;DR

This paper introduces a new aircraft trajectory prediction model that combines several algorithms to improve accuracy and adaptability in complex environments.

## Contribution

The novel ADIBAS-Volterra model integrates improved BAS, ADI, and Volterra series for real-time aircraft trajectory prediction.

## Key findings

- The model achieves a balance between global and local solutions using trained trajectory data.
- K-means clustering improves the algorithm's adaptability by initializing tentacle positions effectively.
- The model's accuracy was validated using RRMSE, MAD, MAPE, and NMSE on Mackey Glass and Rossler datasets.

## Abstract

The prediction of aircraft manoeuvre trajectories is an important prerequisite for decision making. However, how to achieve real-time and scientific aircraft manoeuvre trajectory prediction using trajectory data needs to be addressed urgently. To solve this problem, we propose a hybrid algorithm based on Improved Beetle Antennae Search (BAS), Aircraft Manoeuvre Boundary Point Identification algorithm, Adaptive Dynamic Integration (ADI) and Volterra series, called ADIBAS-Volterra. Firstly, a large amount of trajectory sample data is trained to construct the BAS-Volterra algorithm suitable for predicting aircraft manoeuvre trajectories, which achieves a balance between global and local solutions. Secondly, in order to improve the accuracy of the online manoeuvre trajectory prediction of our proposed model in complex environments, the parameters of the whole prediction model based on the BAS-Volterra algorithm are adaptively updated according to the identification results of the aircraft manoeuvre boundary points, including the optimisation of the algorithmic weights and the optimisation of the parameters. Compared with the existing state-of-the-art methods, the newly proposed aircraft manoeuvre trajectory prediction algorithm adopts K-means clustering to initialise the tentacle position, which can flexibly adjust the search strategy at different stages and make the algorithm more reasonable. Four measures, Relative Root Mean Square Error (RRMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and Normalised Mean Square Error (NMSE) were used to assess prediction accuracy. Finally, the scientific validity of the proposed algorithm is verified using Mackey Glass and Rossler datasets.