Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression

WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present Gaussian process regression models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points. As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure.