J. Bhatia, A. Dayal et al., "Object Classification Technique for mmWave FMCW Radars using Range-FFT Features," 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India, 2021, pp. 111-115, doi: 10.1109/COMSNETS51098.2021.9352894.
In this project we classify the objects based on the features extracted from range FFT from mmWave radar.
The dataset is private. For the dataset please contact me.
- Total samples: 226
- Total classes: 3 ( Car, Drone, Human)
- Total no. of features: 5 ( Distance, Area, Height, Width, Standard Deviation)
In this project 4 machine learning models were apllied on the above mentioned dataset and their performance was evaluated.
- The file Log_reg.ipynb consists of the logistic regression model. This file consists of all the steps from loading the dataset to training and then testing and saing the trained model.
- The trained logistic regression model can be found here https://drive.google.com/file/d/1ZQKDnykqlbd5GMcEdsWefS52N7LU-AIp/view?usp=sharing .
- The file Naive_Bayes.ipynb consists of the Naives Bayes model. This file consists of all the steps from loading the dataset to training and then testing and saing the trained model.
- The trained Naive Bayes model can be found here https://drive.google.com/file/d/1Evh8LaBAtU8L1Dvv4aLxJ0ZtLL9Xte2Z/view?usp=sharing .
- The file SVM.ipynb consists of the SVM model. This file consists of all the steps from loading the dataset to training and then testing and saing the trained model.
- The trained SVM model can be found here https://drive.google.com/file/d/1TdNiztHQeX9s5hQBd_b492t7XgtFhJIS/view?usp=sharing .
- The file LGBM.ipynb consists of the LightGBM model. This file consists of all the steps from loading the dataset to training and then testing and saing the trained model.
- The trained LightGBM model can be found here https://drive.google.com/file/d/1UDFleIO1_bMx8t0sF5tpvt4eR-vkgCZ4/view?usp=sharing .