RSS-Based Indoor Positioning Based on Multi-Dimensional Kernel Modeling and Weighted Average Tracking

In this paper, we discuss a similarity inconsistency phenomenon where the radio signal strength (RSS) signatures of two neighboring positions are dissimilar due to the RSS variation. While matching an observed RSS throughout the radio map, the phenomenon would lead to a jagged similarity distribution. This may break the similarity assumption of the previous works. To address the problem, we proposed a multi-dimensional kernel density estimation (MDKDE) method. By introducing the spatial kernel, the method could adopt neighboring information to enrich the fingerprint. The model can also help to generate a smooth and consistent similarity distribution. Moreover, we formulated the searching of the target location over the continuous domain as an optimization problem. Instead of estimating the optimal location numerically, we also came up with an efficient tracking method, weighted average tracker (WAT). Upon the MDKDE model, WAT can track the target in a simple weighted average method. The experimental results have demonstrated that the proposed system could well model the RSS variation and provide robust positioning performance in an efficient manner.