Vehicle Speed Estimation Based on Sensor Networks and Signal Correlation Measurement

This paper proposed a novel method for accurate vehicle speed estimation based on magnetic sensors. The estimation system consists of triple sensors and signals are collected synchronously when vehicles travel over it. Taking into consideration the difference of sensor sensitivity and self-disturbance of Earths magnetic field, a signal correlation model is introduced to improve the measurement precision of vehicle traveling time. Spectrum analysis and correlation model are used to accurately estimate the phase difference of sensor signals. In addition, an efficient clock synchronization algorithm based on active compensation is designed to reduce the time estimation error and enhance the vehicle speed estimation accuracy. Simulation and on-road experiment show that the method introduced in this paper has better performance and robustness than other approaches.

[1]  Fei-Yue Wang,et al.  RHODES to Intelligent Transportation Systems , 2005, IEEE Intell. Syst..

[2]  Sing Yiu Cheung,et al.  Traffic Surveillance by Wireless Sensor Networks: Final Report , 2007 .

[3]  Nisheeth Shrivastava,et al.  Target tracking with binary proximity sensors , 2009, TOSN.

[4]  Manuel R. Arahal,et al.  Current paradigms in intelligent transportation systems , 2010 .

[5]  J. W. C. van Lint,et al.  Advanced traffic monitoring for sustainable traffic management: Experiences and results of five year , 2010 .

[6]  Lawrence A Klein,et al.  SUMMARY OF VEHICLE DETECTION AND SURVEILLANCE TECHNOLOGIES USED IN INTELLIGENT TRANSPORTATION SYSTEMS , 2000 .

[7]  Gábor Stépán,et al.  Traffic jams: dynamics and control , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Pravin Varaiya,et al.  Wireless magnetic sensors for traffic surveillance , 2008 .

[9]  Upamanyu Madhow,et al.  Multiple-Target Tracking With Binary Proximity Sensors , 2011, TOSN.

[10]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Li Li,et al.  Cooperative node localization using nonlinear data projection , 2009, TOSN.

[12]  M R Flynn,et al.  Self-sustained nonlinear waves in traffic flow. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Saurabh Ganeriwal,et al.  Timing-sync protocol for sensor networks , 2003, SenSys '03.

[14]  Fikret Sivrikaya,et al.  Time synchronization in sensor networks: a survey , 2004, IEEE Network.