Received Signal Strength Quantization for Secure Indoor Positioning via Fingerprinting

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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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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