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.
