Harvesting Indoor Positioning Accuracy by Exploring Multiple Features From Received Signal Strength Vector

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

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Characteristic analysis of wireless local area network's received signal strength indication in indoor positioning

IET Communications ◽

10.1049/iet-com.2019.0681 ◽

2020 ◽

Vol 14 (3) ◽

pp. 497-504 ◽

Cited By ~ 1

Author(s):

Minmin Lin ◽

Baoxing Chen ◽

Wenjie Zhang ◽

Jingmin Yang

Keyword(s):

Signal Strength ◽

Local Area ◽

Indoor Positioning ◽

Received Signal Strength ◽

Characteristic Analysis ◽

Received Signal Strength Indication

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Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Computer Networks ◽

10.1016/j.comnet.2015.08.032 ◽

2015 ◽

Vol 91 ◽

pp. 329-340 ◽

Cited By ~ 12

Author(s):

Jenq-Shiou Leu ◽

Min-Chieh Yu ◽

Hung-Jie Tzeng

Keyword(s):

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Positioning Precision

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Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Data ◽

10.3390/data5030067 ◽

2020 ◽

Vol 5 (3) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Fernando J. Aranda ◽

Felipe Parralejo ◽

Fernando J. Álvarez ◽

Joaquín Torres-Sospedra

Keyword(s):

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Location Based Services ◽

Positioning System ◽

Positioning Accuracy ◽

Bluetooth Low Energy ◽

Outdoor Environments ◽

The University

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

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Analysis of Received Signal Strength Quantization in Fingerprinting Localization

Sensors ◽

10.3390/s20113203 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3203 ◽

Cited By ~ 2

Author(s):

Syed Khandker ◽

Joaquín Torres-Sospedra ◽

Tapani Ristaniemi

Keyword(s):

Quality Of Service ◽

Indoor Localization ◽

Signal Strength ◽

Received Signal Strength ◽

Storage Space ◽

Data Traffic ◽

Positioning Accuracy ◽

Hardware Configuration ◽

Fine Resolution

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

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Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1601-3 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Qing Yang ◽

Shijue Zheng ◽

Ming Liu ◽

Yawen Zhang

Keyword(s):

Nearest Neighbor ◽

Science And Technology ◽

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Experimental Simulation ◽

K Nearest Neighbor ◽

Improvement Strategy ◽

Fingerprint Algorithm ◽

Received Signal Strength Indication

AbstractTo improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

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