Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning

Area of Science:

• Space exploration and robotics
• Signal processing and machine learning
• Optical sensing technologies

Background:

• Accurate classification of non-cooperative targets is critical for space mission safety and success.
• Existing methods for target identification may lack efficiency or accuracy in complex space environments.
• The micro-Doppler effect and laser coherence detection offer unique signatures for target characterization.

Purpose of the Study:

• To develop and validate an efficient deep learning-based method for classifying and recognizing non-cooperative space targets.
• To leverage the principles of micro-Doppler effect and laser coherence detection for enhanced target identification.
• To demonstrate high accuracy and robustness of the proposed method through simulations and experiments.

Main Methods:

• Utilizing deep learning algorithms for pattern recognition in target signatures.
• Applying micro-Doppler effect analysis to differentiate targets based on their motion characteristics.
• Employing laser coherence detection to gather detailed information about target properties.
• Conducting theoretical simulations and experimental verification to validate the method's performance.

Main Results:

• Achieved 100% accuracy in classifying different non-cooperative targets after a single training round.
• Demonstrated stable 100% accuracy in recognizing targets across various attitude angles after 10 training rounds.
• Validated the efficiency and effectiveness of the deep learning approach combined with micro-Doppler and laser coherence principles.

Conclusions:

• The proposed deep learning method provides a highly accurate and efficient solution for non-cooperative target classification and recognition in space missions.
• The integration of micro-Doppler effect and laser coherence detection significantly enhances target identification capabilities.
• This approach holds significant promise for improving the safety and autonomy of future space operations.