Publications scientifiques
Autres affiliations : University of Johannesburg
Ange Joel Nounga Njanda
Jocelyn Edinio Zacko Gbadoubissa
Emanuel Rădoi* (Auteur correspondant)
Autres affiliations : Université de Bretagne Occidentale · Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance
Roua Youssef
Autres affiliations : Université de Bretagne Occidentale · Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance
Ado Adamou Abba Ari
Autres affiliations : University of Maroua · Université Paris-Saclay · Université de Garoua
Systems and Soft Computing
This study aims to detect and count people using impulse radio ultra-wideband radar and machine learning algorithms. However, the data quality, difficulty distinguishing human signals from noise and clutter, and instances where human presence is not detected make it challenging to count multiple humans. To overcome these challenges, we apply wavelet transformation to reduce signal size and use simple moving averages to eliminate noise. Next, we create features based on statistical and entropic properties of the signal and apply several classification algorithms, including ANN, Random Forest, KNN, XGBOOST, and multiple linear regression, to predict the number of people present. Our findings reveal that using the ANN classifier with the Daubechies 4 (db4) wavelet provides better results than other classifiers, with an accuracy rate of 99%. Additionally, filtering the data improves accuracy, and labeling the data after extracting essential characteristics significantly improves the model’s accuracy.
