Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones: A Maximum Likelihood Approach | EURASIP Journal on Applied Signal Processing 2004 8 1059-1077 2004 Hindawi Publishing Corporation Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones A Maximum Likelihood Approach Roberto Lopez-Valcarce Departamento de Teoria de la Senal y las Comunicaciones Universidad de Vigo 36200 Vigo Spain Email valcarce@ Carlos Mosquera Departamento de Teoria de la Senal y las Comunicaciones Universidad de Vigo 36200 Vigo Spain Email mosquera@ Fernando Perez-Gonzalez Departamento de Teoria de la Senal y las Comunicaciones Universidad de Vigo 36200 Vigo Spain Email fperez@ Received 4 July 2003 Revised 25 September 2003 Recommended for Publication by Jacob Benesty We address the problem of estimating the speed of a road vehicle from its acoustic signature recorded by a pair of omnidirectional microphones located next to the road. This choice of sensors is motivated by their nonintrusive nature as well as low installation and maintenance costs. A novel estimation technique is proposed which is based on the maximum likelihood principle. It directly estimates car speed without any assumptions on the acoustic signal emitted by the vehicle. This has the advantages of bypassing troublesome intermediate delay estimation steps as well as eliminating the need for an accurate yet general enough acoustic traffic model. An analysis of the estimate for narrowband and broadband sources is provided and verified with computer simulations. The estimation algorithm uses a bank of modified crosscorrelators and therefore it is well suited to DSP implementation performing well with preliminary field data. Keywords and phrases speed estimation traffic monitoring microphone arrays. 1. INTRODUCTION Nowadays several alternatives exist for collecting numerical data about the transit of road vehicles at a given location. From these data parameters such as traffic density and flow are estimated in order to develop effective traffic management .
