Research on an Adaptive Algorithm for Indoor Bluetooth Positioning

Abstract

Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25[Formula: see text]m as the target node and reference node is less than 1.5[Formula: see text]m far from each other. As the distance increases to more than 1.5[Formula: see text]m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.

Filtering Effect on RSSI-Based Indoor Localization Methods

“…Figure 2 where PRX [dB] is the received RSSI value, PL(d0) is the path loss value for a reference distance d0, η is the path loss exponent, and Xσ is a Gaussian random variable with zero mean and variance, σ2, that models the random variation of the RSSI value. The received signal power is affected by attenuation, multipath, reflection, fading, interference, noise and shadowing (Luo et al, 2018) . The position of the tag is likely to be erroneous in this way because the point of intersection is affected by the RSS value.…”

“…WiFi consumes large amount of power, which could easily deplete battery powered devices, hence, not ideal for positioning systems (Sadowski & Spachos, 2018). The emergency of inexpensive IoT devices and applications like Bluetooth Low Energy (BLE) (Luo et al, 2018), Zigbee and beacons, has made it easy to arrange the devices for indoor positioning.…”

Quantitative analysis on in vivo tumor‐microvascular images from optical‐resolution photoacoustic microscopy

“…Here, the time of flight is the distance between the source point and other points in the blood vessel, and the point with the maximum distance is called the furthest point. The gradient field is calculated from the time of flight image by central difference method [26].…”

Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

“…Despite the soundness of the various approaches elucidated above, the indoor position system is far from real-world application. Generally speaking, Wi-Fi can be distinguished from the other approaches by its aspect of wide deployment but suffers from a huge amount of energy consumption [17]. The methods based on RFID have the advantages of high accuracy, anti-interference and disadvantage of compatibility.…”