Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning

Abstract

Fingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positioning system deploys. This requires a heavy workload to build accurate systems, causing a tradeoff between accuracy and practicality. In this article, we propose a chain of subsequent preprocessing techniques for generating accurate radio frequency maps (RMs). The techniques consist of filtering the received signal strength indicator and interpolating the local probability distribution parameters. The proposed subsequent techniques generate smoother RMs and describe these maps with only two parameters per position. By plugging an adaptive particle filter as the position estimation algorithm, we show that the generated RMs increase the positioning accuracy significantly. We also investigate the relation between practicality and accuracy in terms of the invested time in the process of fingerprinting and the stored data to represent the RM. Alongside the increased accuracy of the proposed system, the approach allows a dramatic increase in the practicality of the fingerprinting technique.