Possibilistic modeling of ultrasonic signal for floor state recognition

The process of staircases detection and recognition is complex for blinds. Therefore, an intelligent and real time system is required to help them. In this paper, we investigate using only one ultrasonic sensor and few samples with small size to represent floor and staircases. The performance of such system depend on object representation, data modeling and finally classification algorithm. A simple wave analysis have shown that frequency components are the most affected in stair case context. Accordingly, we have used frequency representation of ultrasonic signal, namely the smoothed periodogram. Then, we model model several extracted features based on Masson possibility approach. Finally, similarity measure is used in the classification algorithm. A training process is under taken on a local database of 500 signal simples is used. An accuracy rate of 94% has been achieved.