A recursive multi-scale correlation-averaging algorithm for synchronization and fusion of independent pavement roughness measurements

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 corrupted by both sensor noise and GPS positioning uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and 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 monitoring. Our method and the related signal processing algorithms are tested using field data.