Abstract
This paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithm is introduced to provide reliable position estimates by integrating motion sensor-based positioning (i.e., dead-reckoning) and RSS-based fingerprinting positioning with Kalman filter. In the presence of the RSS variation, RSS-based fingerprinting positioning provides unreliable results due to different characteristics of RSS measurements in the offline and online phases, and the tracking performance is degraded. To mitigate the effect of the RSS variation, a recursive least square estimation-based self-calibration algorithm is proposed that estimates the RSS variation parameters and provides the mapping between the offline and online RSS measurements. By combining the Kalman filter-based tracking algorithm with the self-calibration, the proposed algorithm can achieve higher tracking accuracy even in severe RSS variation conditions. Through extensive computer simulations, we have shown that the proposed algorithm outperforms other position tracking algorithms without self-calibration.
Highlights
The indoor position tracking has received a great deal of attention for location-based services such as indoor navigation, retail, and entertainment services [1]
In the received signal strength (RSS)-based fingerprinting positioning, the position of a mobile device is estimated by measuring the similarities between the currently obtained RSS measurements and the ones in a prebuilt radio map. (The radio map consists of a set of RSS observations from access points (APs) at reference points (RPs) with prior position information
Several algorithms were proposed to mitigate the effect of the RSS variation by mapping the online RSS measurements to the offline measurements based on a linear fitting model [11,12,13, 15,16,17,18]
Summary
The indoor position tracking has received a great deal of attention for location-based services such as indoor navigation, retail, and entertainment services [1]. Several algorithms were proposed to mitigate the effect of the RSS variation by mapping the online RSS measurements to the offline measurements based on a linear fitting model [11,12,13, 15,16,17,18]. This is referred to as the calibration. We propose a novel Kalman filter-based indoor position tracking algorithm with self-calibration to provide more accurate positioning results under the RSS variation.
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