Numerical analysis: 4th ed
Discrete-time signal processing
Discrete-time signal processing
Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
EURASIP Journal on Applied Signal Processing
Gaussian Approximations for Energy-Based Detection and Localization in Sensor Networks
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Explicit Ziv-Zakai lower bound for bearing estimation
IEEE Transactions on Signal Processing
Proceedings of the 8th international conference on Mobile systems, applications, and services
Hi-index | 35.70 |
We estimate a vehicle'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's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal'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's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint 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.