Enhanced propagation model constrained RSS fingerprints patching with map assistance for Wi-Fi positioning

Wi-Fi fingerprint-based positioning is widely used caused by its low cost and ease of implementation when Global Positioning System technology struggles to obtain signal and accuracy in indoor environments. A prerequisite for Wi-Fi fingerprint-based positioning is the availability of an accurate fingerprint database. However, in practice, access to a large proportion of the area is restricted, leading to a rapid degradation of the positioning accuracy of existing interpolation techniques in the absence of reference data in the vicinity of the area. In this paper, we propose an enhanced propagation model constrained fingerprint patching algorithm for incomplete RSS fingerprint databases, aiming to extend the coverage of fingerprints and reduce the overall workload of survey. The algorithm integrates a map-assisted wireless signal propagation models with incomplete spatially sampled fingerprint data to construct a propagation model for each Wi-Fi access point (AP). Recalculate the received signal strength at each reference point for each AP in the fingerprint database according to the specific model. The fingerprint information for the unrecorded regions is then refined by interpolation and the fingerprints initially collected are optimized. We experimentally validate the effectiveness of the proposed interpolation algorithm and compare it with the conventional Support Vector Regression (SVR), Gaussian Process Regression (GPR), and rational quadratic kernel (RQK) methods. The results show that the positioning performance of proposed method is improved by 18.1%, 23.1% and 39.7% in the blank area relative to RQK, GPR and SVR, respectively. In addition, our method outperforms other algorithms with stable performance in different scenarios, especially in scenarios containing obstacle walls, and the performance benefit appears more notable in the complicated indoor scenario with cramped rooms.