On the Dynamic RSS Feedbacks of Indoor Fingerprinting Databases for Localization Reliability Improvement

163

Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated “weighted trilateration”. The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutions.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Paterna

Calveras

Paradells

et al. 2017

163

“…(1) The fingerprint database established at each location in the target positioning area is acquired during the offline training phase, and this database needs to be updated constantly over time with changes in the environment. (2) The positioning accuracy varies with the dynamic environment [6]. As mentioned, RSS is vulnerable to issues, such as multiple paths, shadow effects, and people’s actions.…”

Section: Preliminariesmentioning

confidence: 99%

“…However, doing so is costly and time-consuming when the positioning area is very large. In particular, it is impractical to continually update the fingerprint database in an area where the interference continues to change [6].…”

Section: Introductionmentioning

confidence: 99%

Localization Reliability Improvement Using Deep Gaussian Process Regression Model

Teng

Tao

Own

2018

Self Cite

With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The method that is based on received signal strength (RSS) is the most widely used. However, manually measuring RSS signal values to build a fingerprint database is costly and time-consuming, and it is impractical in a dynamic environment with a large positioning area. In this study, we propose an indoor positioning system that is based on the deep Gaussian process regression (DGPR) model. This model is a nonparametric model and it only needs to measure part of the reference points, thus reducing the time and cost required for data collection. The model converts the RSS values into four types of characterizing values as input data and then predicts the position coordinates using DGPR. Finally, after reinforcement learning, the position coordinates are optimized. The authors conducted several experiments on a simulated environment by MATLAB and physical environments at Tianjin University. The experiments examined different environments, different kernels, and positioning accuracy. The results showed that the proposed method could not only retain the positioning accuracy, but also save the computation time that is required for location estimation.

“…Nowadays, with the rapid development of computing technology, the demand for location based services (LBS) is rapidly increasing [1]. Global navigation satellite systems (GNSS) have been successfully used for outdoor scenarios; however, it is difficult to use it indoors because of signal attenuation [2].…”

Section: Introductionmentioning

confidence: 99%

A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model

Lai

et al. 2016

Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.