research-article

Fast Prediction Method for Drop Point Coordinates Based on Neural Network Algorithm

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Song FengAuthors Info & Claims

AAIA '23: Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

Pages 22 - 26

https://doi.org/10.1145/3603273.3628764

Published: 09 January 2024 Publication History

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Abstract

The parameter of the ending damage at the end of the projectile is important for the judgment destruction of the weapon. The parameters are tested by a high -speed camera. However, the spreading large -scale scattered splits of the projectile, and small field of high-speed cameras have result of difficulty in testing. In order to adjust the test area and improve the data admission rate of the damage parameter test, we proposed a fast point coordinate prediction method based on neural network algorithm. The neural network algorithm can be fused with data from multi-sources, that is, different angles and ranges can be trained together. The simulation results demonstrate the effectiveness of the prediction method based on neural network.

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Shi Jinguang, Xu Mingyou, Wang Zhongyuan. Waiting for the application of Calman filtering in the bombing point calculation [J]. Board Journal, 2008, 20 (3): 41-48.

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Zhang Cheng. Pulse correction of ammunition range prediction control method [J]. True Journal, 2010,22 (1): 20-23.

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Index Terms

Fast Prediction Method for Drop Point Coordinates Based on Neural Network Algorithm
1. Applied computing
  1. Computers in other domains
    1. Military

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AAIA '23: Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

November 2023

406 pages

ISBN:9798400708268

DOI:10.1145/3603273

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 09 January 2024

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AAIA 2023

AAIA 2023: 2023 International Conference on Advances in Artificial Intelligence and Applications

November 18 - 20, 2023

Wuhan, China

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Abstract

References

Index Terms

Recommendations

Prediction of Sunspot Series Using BiLinear Recurrent Neural Network

Modelling and Prediction of Internet Time-Delay by Feed-Forward Multi-layer Perceptron Neural Network

Moving Object Tracking Method Based on n-Step-ahead Prediction Using Artificial Neural Network Algorithm

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