Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

Abstract

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.

Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting

“…To compare k-NN and improved variants we applied them over 51 Wi-Fi datasets: DSIn [27], LIBn [28], MANn [29], MINT1 [30], SAH1 & TIE1 [31], TUTn [32]- [37], UJIn [38], UTS [39], OFIN [40], GPRn [41], SODn [14], KIOSn [42], EEILn [43]; 9 BLE datasets: OFINBn [40], UEXBn [44], UJIBn [45]; and 9 hybrid datasets: HDBn [46]. The implemented methods and dataset descriptions are provided in [20] for research reproducibility and replicability [47].…”