https://github.com/diptiman-mohanta/radar-based-uav-classification
Advanced radar-based classification system for detecting and distinguishing UAVs, birds, and RC aircraft using SVMD signal decomposition and deep learning feature extraction.
https://github.com/diptiman-mohanta/radar-based-uav-classification
deep-learning drone-bird-classification drone-detection matlab radar-signal-processing signal-processing spectrogram-analysis squeezenet svmd time-frequency-analysis uav-detection variational-mode-decomposition
Last synced: 3 months ago
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Advanced radar-based classification system for detecting and distinguishing UAVs, birds, and RC aircraft using SVMD signal decomposition and deep learning feature extraction.
- Host: GitHub
- URL: https://github.com/diptiman-mohanta/radar-based-uav-classification
- Owner: diptiman-mohanta
- Created: 2025-06-16T06:06:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-07-07T16:29:51.000Z (3 months ago)
- Last Synced: 2025-07-07T17:49:57.561Z (3 months ago)
- Topics: deep-learning, drone-bird-classification, drone-detection, matlab, radar-signal-processing, signal-processing, spectrogram-analysis, squeezenet, svmd, time-frequency-analysis, uav-detection, variational-mode-decomposition
- Language: MATLAB
- Homepage:
- Size: 89.8 KB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
A sophisticated radar-based object classification system that distinguishes between UAVs (drones), birds, RC aircraft, and mixed scenarios using advanced signal processing techniques and deep learning.
# Features
Advanced Signal Processing: Implementation of Successive Variational Mode Decomposition (SVMD) for superior signal analysis
Cross-term-free Spectrograms: Enhanced time-frequency representations for better classification accuracy
Transfer Learning: Utilizes pre-trained SqueezeNet for efficient feature extraction
Multi-class Classification: Distinguishes between drones, birds, RC planes, and mixed scenarios
Automated Pipeline: End-to-end processing from raw radar data to classification result
# Methodology
Signal Processing Pipeline
Preprocessing: Downsampling, resampling, and low-pass filtering
SVMD Decomposition: Successive decomposition into Intrinsic Mode Functions (IMFs)
Spectrogram Generation: Cross-term-free STFT computation
Feature Extraction: Deep CNN features using SqueezeNet
Classification: Multi-class object identification
# Key Algorithms
SVMD (Successive Variational Mode Decomposition): Advanced signal decomposition technique
VMD (Variational Mode Decomposition): Standard mode decomposition for comparison
Transfer Learning: Pre-trained CNN feature extraction
# Prerequisites
MATLAB R2020b or later (using 2023b)
Signal Processing Toolbox
Deep Learning Toolbox
Image Processing Toolbox
## Installation
```bash
git clone https://github.com/diptiman-mohanta/Radar-Based-UAV-Classification.git
cd Radar-Based-UAV-Classification
```
# Citation
If you use this work in your research, please cite:
```bibtex
@misc{radar_uav_classification,
title={Radar-Based UAV Classification using SVMD, Spectogram and Deep Learning},
author={Diptiman Mohanta and Akash S R and Shekh Sharfraj and Krishna Jyoti Panda and Arpita Pradhan and Jyotirmayee Patnaik},
year={2025},
url={https://github.com/diptiman-mohanta/Radar-Based-UAV-Classification.git}
}
```
# Dataset
```bibtex
@data{1x2q-8v62-22,
doi = {10.21227/1x2q-8v62},
url = {https://dx.doi.org/10.21227/1x2q-8v62},
author = {Harish Chandra Kumawat and Mainak Chakraborty and A. Arockia Bazil Raj and Sunita Vikrant Dhavale},
publisher = {IEEE Dataport},
title = {DIAT-µSAT: micro-Doppler Signature Dataset of Small Unmanned Aerial Vehicle (SUAV)},
year = {2022} }