iBeacon/WiFi Signal Characteristics Analysis for Indoor Positioning Using Mobile Phone

Yang, Ling; Li, Bofeng; Li, Haojun; Shen, Yunzhong

doi:10.1007/978-981-10-4588-2_35

Cited by 3 publications

(6 citation statements)

References 16 publications

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“…The fusion of trilateration and fingerprinting effectively curbs the divergence of location error (see Equation 12 The BLE-based method provides the baseline accuracy for the whole fusion positioning approach. According to a related study [12] comparing the positioning accuracy of iBeacons and WiFi APs, WiFi APs outperform iBeacons on fingerprinting positioning accuracy. This study has pointed out the accuracy of WiFi positioning is 5 m 90% of the time but that of iBeacon is 5 m less than 70% of the time (Figure 16a).…”

Section: Discussionmentioning

confidence: 99%

“…In this way, after the location estimates of the two methods are generated, we can compute their distances to the last location. Assuming the resulting distance of trilateration is d 1 and that of fingerprinting is d 2 , we list their weights for fusion in Equation (12). A long distance relates to a small weight when the both distances are equal to or larger than 1 m. In contrast, a long distance gains a large weight when d 1 and d 2 are both shorter than 1 m. Based on the coordinates derived by the both methods, we employ these weights in Equation (12) to obtain the weighted coordinates of the current location.…”

Section: Ble-based Methodsmentioning

confidence: 99%

“…It is ideal to support smartphone applications. However, previous research shows positioning based on iBeacon has a relatively lower accuracy than WiFi APs [12], due to the attenuation property of iBeacon signals.…”

Section: Introductionmentioning

confidence: 93%

“…Comparison of CDF between a previous study and this paper: (a) CDF of positioning accuracy of iBeacons and WiFi APs[12]; and (b) CDF of our BLE-based method.…”

mentioning

confidence: 97%

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Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Liu

Yang

et al. 2020

Applied Sciences

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For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

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Section: Discussionmentioning

confidence: 99%

Section: Ble-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

“…Comparison of CDF between a previous study and this paper: (a) CDF of positioning accuracy of iBeacons and WiFi APs[12]; and (b) CDF of our BLE-based method.…”

mentioning

confidence: 97%

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Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Liu

Yang

et al. 2020

Applied Sciences

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“…The development of BLE technology has attracted great interest in recent years, and it is considered by some researchers to be a more advantageous indoor signal source than Wi-Fi. Several studies have analyzed the signal characteristics of BLE and Wi-Fi and used the same positioning algorithm to compare the different positioning results obtained via different signal sources [10,11]. Compared to Wi-Fi, the iBeacon—a BLE implementation protocol from Apple [12]—has the characteristics of low power consumption, a smaller device size, better anti-inference systems, and easy arrangement.…”

Section: Introductionmentioning

confidence: 99%

Probability-Based Indoor Positioning Algorithm Using iBeacons

Xia

Liu

et al. 2019

Sensors

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High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.

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Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and Update

Yang

2021

IEEE Trans. Wireless Commun.

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iBeacon/WiFi Signal Characteristics Analysis for Indoor Positioning Using Mobile Phone

Cited by 3 publications

References 16 publications

Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Probability-Based Indoor Positioning Algorithm Using iBeacons

Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and Update

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