Abstract
A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.
Original language | English |
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Article number | 5752854 |
Pages (from-to) | 795-808 |
Number of pages | 14 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 12 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2011 |
Funding
Manuscript received April 5, 2010; revised October 15, 2010 and January 9, 2011; accepted January 30, 2011. Date of publication April 21, 2011; date of current version September 6, 2011. This work was supported in part by the Motorola Foundation and in part by the Joint Transportation Research Program administrated by the Indiana Department of Transportation and Purdue University. The Associate Editor for this paper was H. Dia.
Funders | Funder number |
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Indiana Department of Transportation and Purdue University | |
Motorola Foundation |
Keywords
- Data fusion
- data modeling
- data processing
- intelligent systems
- sensor data analytics
- signal processing