Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control
Abstract
:1. Introduction
2. Preliminaries
2.1. Engagement Geometry
2.2. Original Guidance Law
3. Real-Time Bias Computation by Neural Network
3.1. Neural Network Settings and Dataset Generation
3.2. Neural Network Training and Evaluation
4. Simulations
4.1. Trajectory Comparison
4.2. Time Consumption Comparison at Equal Update Frequency
4.3. Update Frequency Comparison at Equal Real-World Time Usage
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Time Cost (s) | 1 Hz | 20 Hz | 100 Hz |
---|---|---|---|
Baseline method | 5.25 | 8.20 | 20.75 |
Proposed method | 0.89 | 0.93 | 0.97 |
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Zhang, X.; Hong, H. Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control. Aerospace 2024, 11, 670. https://doi.org/10.3390/aerospace11080670
Zhang X, Hong H. Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control. Aerospace. 2024; 11(8):670. https://doi.org/10.3390/aerospace11080670
Chicago/Turabian StyleZhang, Xue, and Haichao Hong. 2024. "Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control" Aerospace 11, no. 8: 670. https://doi.org/10.3390/aerospace11080670