@article {12525,
	title = {Vehicle Speed Estimation Using Acoustic Wave Patterns},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {57},
	year = {2009},
	month = {2009/01//},
	pages = {30 - 47},
	abstract = {We estimate a vehicle{\textquoteright}s speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle{\textquoteright}s drive-by noise. The acoustic wave pattern is determined using the vehicle{\textquoteright}s speed, the Doppler shift factor, the sensor{\textquoteright}s distance to the vehicle{\textquoteright}s closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal{\textquoteright}s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle{\textquoteright}s initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a <i>fingerprint</i> that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.},
	keywords = {acoustic, Doppler, estimation;passive, estimation;road, estimation;wheelbase, factor;acoustic, identification;vehicle, length;Doppler, length;vehicle, likelihood, patterns;envelope, processing;maximum, profile, sensor;tire, shape;maximum, shift, shift;acoustic, signal, speed, track, vector;vehicle, vehicles;, wave},
	isbn = {1053-587X},
	doi = {10.1109/TSP.2008.2005750},
	author = {Cevher, V. and Chellapa, Rama and McClellan, J.H.}
}
