Cross-Device Wi-Fi Map Fusion with Gaussian Processes

We investigate the use of linear adaptation when fusing Wi-Fi maps built from spatially sparse received signal strength measurements obtained with multiple devices. First, we show that the residual of the linear regression between devices, usually unaccounted for in existing cross-device localization work, is an important indicator of device dissimilarity and a good predictor of localization performance. Through explicitly modeling the device dissimilarity, one can improve localization accuracy when fusing training sets from multiple devices by weighting each training set differently. Second, we use the Gaussian process ( GP) sensor model to develop a regression algorithm which more reliably estimates the linear fit and device dissimilarity given only a few labeled samples from each new device. By accounting for device dissimilarities in map fusion and by using the proposed regression algorithm, localization performance can be greatly improved given just a few training samples from a new device. Also, when fusing multiple existing maps for a new device using regression misfit, performance is improved by 3.5 to 10 percent.