Ground-Target Recognition Method Based on Transfer Learning.

Area of Science:

• Robotics and Automation
• Signal Processing
• Machine Learning

Background:

• Moving ground-target recognition systems are crucial for monitoring key areas.
• Vibration sensor-based systems offer advantages like small size and low power consumption.
• Existing algorithms struggle with long-distance targets, environmental adaptation, and accuracy.

Purpose of the Study:

• To develop an improved moving ground-target recognition algorithm.
• To enhance adaptability to new environments and boost recognition accuracy.
• To leverage transfer learning for robust target identification.

Main Methods:

• Utilized convolutional neural networks (CNNs) for feature extraction from vibration signals.
• Applied domain adaptation techniques to improve environmental robustness.
• Integrated transfer learning to enhance the algorithm's adaptability.

Main Results:

• The proposed algorithm successfully identifies target types from vibration signals.
• Demonstrated significant improvements in recognition accuracy.
• Showcased effective adaptation to different environmental conditions.

Conclusions:

• The novel algorithm based on CNN and transfer learning offers superior performance for moving ground-target recognition.
• The approach enhances adaptability and accuracy, overcoming limitations of existing methods.
• This work provides a foundation for more advanced multi-modal fusion and transfer learning recognition systems.