Vehicle Type Recognition in WSN Based on ITESP Algorithm

Vehicle type recognition is a demanding application of wireless sensor networks (WSN). In context of applying the sound recognition technology on the vehicle type recognition, in this study, a new feature extraction method is proposed based on the improved time encoded signal processing (ITESP) algorithm. The conventional TESP algorithm, which is effective for the speech signal feature extraction, however, is not suitable for the vehicle sound signal which is more complex. To solve this problem, we design an extensional symbol table with 40 characters according to the characteristic features of the vehicle sound signal, and then construct the one-dimensional S-matrix and the two-dimensional A-matrix respectively based on the symbol stream which is encoded by the symbol table. After that, support vector machine (SVM) is used as the classifier to recognize different vehicle types. The simulation results indicate that the vehicle type recognition systems with ITESP features give better performance compared with the conventional TESP based features.