The Improved Maneuvering Model Algorithm Based on Dynamic Feedback Neural Networks for Online Learning
Pages 207 - 211
Abstract
Tracking of maneuvering targets plays an important role in sea battlefield situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, an hybrid filter algorithm based on dynamic feedback neural networks for online Learning is designed, which embedded the trained neural network with memory function into the state estimation step of the UKF filter to form a hybrid filter. Based on the input eigenvalues, the estimated error is predicted. This estimated error corrects the state estimation in real time and realizes the online monitoring of maneuvering. The simulation results show that the algorithm has a strong adaptability to the target maneuvering form, and has better performance in terms of convergence and filtering accuracy.
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Index Terms
- The Improved Maneuvering Model Algorithm Based on Dynamic Feedback Neural Networks for Online Learning
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
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