A Kernel Method to Nonlinear Location Estimation with RSS-based Fingerprint
This work improves indoor positioning accuracy for smartphone users in dynamic environments, representing an incremental advancement in the field.
The paper tackles the problem of indoor location estimation using RSS-based fingerprints from BLE beacons, addressing variability due to smartphone holding positions, and reports a substantial performance gain over state-of-the-art methods in experiments based on large-scale data from a complex building.
This paper presents a nonlinear location estimation to infer the position of a user holding a smartphone. We consider a large location with $M$ number of grid points, each grid point is labeled with a unique fingerprint consisting of the received signal strength (RSS) values measured from $N$ number of Bluetooth Low Energy (BLE) beacons. Given the fingerprint observed by the smartphone, the user's current location can be estimated by finding the top-k similar fingerprints from the list of fingerprints registered in the database. Besides the environmental factors, the dynamicity in holding the smartphone is another source to the variation in fingerprint measurements, yet there are not many studies addressing the fingerprint variability due to dynamic smartphone positions held by human hands during online detection. To this end, we propose a nonlinear location estimation using the kernel method. Specifically, our proposed method comprises of two steps: 1) a beacon selection strategy to select a subset of beacons that is insensitive to the subtle change of holding positions, and 2) a kernel method to compute the similarity between this subset of observed signals and all the fingerprints registered in the database. The experimental results based on large-scale data collected in a complex building indicate a substantial performance gain of our proposed approach in comparison to state-of-the-art methods. The dataset consisting of the signal information collected from the beacons is available online.