A Distributed Positioning System Based on a Predictive Fingerprinting Method Enabling Sub-Metric Precision in IEEE 802.11 Networks

Abstract

In this paper, we present a distributed positioning system for indoor environment based on a mesh of compact independent anchor nodes. Each node is built of low-cost off-the-shelf components and operates as specialized access points capable of standard connectivity at 2.45 GHz. The key technology for the localization strategy is the switched beam antenna (SBA), which enables a space division multiple access (SDMA) paradigm. The coordinated operation of SBA-equipped anchor nodes constitutes a legacy unmodified IEEE 802.11 network which can exploit the multiplexing mechanism. The latter is the driving force of the estimation strategy, with the positional information obtained as the result of a maximum likelihood algorithm driven by the comparison of a real-time received signal strength indicator (RSSI) with the predicted signal level distribution, which can be estimated and stored without the need of lengthy offline measurement. The signal level prediction is based on a simple propagation model which is effective because benefits of both the elementary antenna radiation beams directivity and the circular polarization operation, two strong aids for the mitigation of the multipath impairment. In turn, these feature make the estimation procedure tolerant to noisy power measurements, hence particularly suitable for cost-effective solutions based on RSSI. Experimental validations demonstrate the performance of a network composed of four anchors arranged in a 2.6 $\,\times\, $ 3.8 m $^{2}$ mesh in a 6 $\,\times\, $ 7 m $^{2}$ office room, and dealing with a single target node. The mean error inside the mesh area is 63 cm while the mean error in the entire room is 1.1 m. Focusing on the cumulative distribution of the error, the 90th percentile value is 1 m considering only the mesh and 1.9 m for the entire room.