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Published on: December 15, 2023
Model-based neural network for target detection in SAR images
L I Perlovsky1, W H Schoendorf, B J Burdick
1Nichols Res. Corp., Lexington, MA.
Summary
This study introduces model-based neural networks that integrate a priori knowledge with adaptive learning for improved intelligence research. Applications in synthetic aperture radar (SAR) target detection demonstrate the effectiveness of this approach.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Signal Processing
Background:
- The integration of a priori knowledge and adaptive learning in artificial intelligence presents significant mathematical challenges.
- Previous attempts to combine these approaches have encountered difficulties.
Purpose of the Study:
- To introduce a novel model-based neural network architecture.
- To demonstrate the application of this architecture in synthetic aperture radar (SAR) image analysis, specifically for target detection.
Main Methods:
- Development of physics-based models for SAR signals.
- Design of neural networks that leverage these a priori models for adaptive learning.
- Evaluation using real-world SAR image datasets.
Main Results:
- The proposed model-based neural networks effectively utilize a priori models for adaptive learning.
- Successful application demonstrated in target detection within SAR images.
- Validation through several real-world examples.
Conclusions:
- Model-based neural networks offer a viable solution to integrating a priori knowledge with adaptive learning.
- This approach shows significant promise for enhancing intelligence research and specific applications like SAR target detection.