UWB radar recognition system based on HOS and SVMs
Résumé
This study proposes an original ultra-wideband short-range radar (UWB-SRR) recognition system based on higher-order statistics (HOS) and support vector machines (SVMs). The main purpose of this work is to improve the road safety by implementing these techniques for detection and recognition of the uncovered road users such as pedestrians and cyclists. The combination of HOS and cell-averaging constant false alarm rate (CA-CFAR) radar detector has been proposed and investigated. The results show that a combination of HOS and CA-CFAR promises a good performance for UWB radar detector. The authors have also evaluated the performance of SVM-based target recognition system using normalised radar signature as input features. A total of 1000 signatures have been extracted for each class including pedestrian, cyclist, and car, where 50% of them have been used for the training data and the rest for the validation data. The results show that the SVM gives a good performance for the proposed system, where the recognition rates are up to 96.23, 95.25 and 97.23% for the cyclist, pedestrian and car. In the real testing performance using their scenarios, the system has successfully identified 92.77% of the right cyclist, 90.82% of the right pedestrian and 90.73% of the right car.