Orbital Behavior Intention Recognition for Space Non-Cooperative Targets Under Multiple Constraints
To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to mu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/6/520 |
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| Summary: | To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to multiple constraints. The relative orbital motion of the targets is characterized and categorized through the use of Clohessy–Wiltshire equations, forming the foundation of a constrained intention dataset employed for training and evaluation. Furthermore, the method incorporates a composite architecture combining a convolutional neural network (CNN), long short-term memory (LSTM) unit, and self-attention (SA) mechanism to enhance recognition performance. The experimental results demonstrate that the integrated CNN-LSTM-SA model attains a recognition accuracy of 98.6%, significantly surpassing traditional methods and neural network models. Additionally, it demonstrates high efficiency, indicating significant promise for practical applications in avoiding spacecraft collisions and performing orbital maneuvers. |
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| ISSN: | 2226-4310 |