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
Reference Video
For a better understanding of the overall approach and implementation, please refer to the following video:
👉 Video Tutorial
Note: The final implementation in this repository may not exactly match the one demonstrated in the video. However, the project is largely inspired by the concepts and methodology presented there, and the video can be used as a helpful resource for understanding the underlying ideas and workflow.
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
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:
@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
@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} }
to use the dataset i have used mail to the authors to provide the .mat files you can get the email id in this [https://ieee-dataport.org/documents/diat-msat-micro-doppler-signature-dataset-small-unmanned-aerial-vehicle-suav]
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