Problem
A radar classification system needed to distinguish between targets using pulse-Doppler radar data.
Constraints
The solution had to handle noisy radar signals and provide fast inference for real-time use cases.
Approach
Developed a deep learning classification pipeline using Python, PyTorch, SciPy, and signal-processing features derived from radar spectrograms.
Result
Built a working classifier prototype with documented validation criteria and fast-inference design. Performance evaluated against noisy radar signals with clear constraints documented for deployment.
Commercial relevance
Useful for radar-based detection, tracking, and classification systems where speed and accuracy are critical.
Stack
- Python
- PyTorch
- SciPy
- Matplotlib
Confidentiality
High-level summary only, with no sensitive defense or radar system specifics.
Next step
For project inquiries or a confidential scope review, see Services and Contact.