Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones: A Maximum Likelihood Approach
- PDF / 2,051,682 Bytes
- 19 Pages / 600 x 792 pts Page_size
- 87 Downloads / 200 Views
Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones: A Maximum Likelihood Approach ´ Roberto Lopez-Valcarce Departamento de Teor´ıa de la Se˜nal y las Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain Email: [email protected]
Carlos Mosquera Departamento de Teor´ıa de la Se˜nal y las Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain Email: [email protected]
´ ´ Fernando Perez-Gonz alez Departamento de Teor´ıa de la Se˜nal y las Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain Email: [email protected] 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 strategies. Thus, traffic management schemes heavily depend on an infrastructure of sensors capable of automatically monitoring traffic conditions. The design of such systems must include the choice of the type of sensor and the development of adequate signal processing and estimation algorithms [1]. Cheap sensor-based networks enable dense spatial sampling on a road grid, so that meaningful global results can be extracted; this is the so-called collaborative information processing paradigm [2], an emerging interdisciplinary research area tackling different issues such as data fusion, adaptive systems, low power communication and computation, and so forth.
Traffic sensors commercially available at present include magnetic induction loop detectors; radar, infrared, or ultrasound-based detectors; video cameras and microphones. All of them present different characteristics in terms of robustness to changes in environmental conditions; manufacture, installation, and repair costs; safety regulation compliance, and so forth. A desirable system would (i) be passive, to avoid radiation emissions and/or operate at low power; (ii) operate in a
Data Loading...
