Human Detection Based on Learning and Classification of Radio Scattering Parameters and Para-Hermitian Eigenvalue Decomposition
Frank E. Ebong, Nicola Novello, and Andrea M. Tonello
Official repository of the paper " Human Detection Based on Learning and Classification of Radio Scattering Parameters and Para-Hermitian Eigenvalue Decomposition " published at IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2024.
(Coming soon...)
The directory where the scripts are must contain an additional folder Datasets containing 3 folders: Lambdas, Cauchy, and Raw. Lambdas and Cauchy contain the .mat files for the datasets of 0,1, and 2 people obtained using the corresponding pre-processing algorithms. Raw contains 3 folders (one for each class): Empty, Person, and Two_People that contain the s4p files obtained from the Matlab part.
The file main.py runs the experiments.
python3 main.py --mode Lambdas
Where "mode" identifies the pre-processing algorithm used, which can be: Lambdas, Cauchy, No.
The files main_functions.py, classes.py, and utils.py comprise the needed methods and classes.
If you use your code for your research, please cite our paper (coming soon):
The implementation is based on / inspired by: