Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

Subedi, Santosh; Gang, Hui-Seon; Ko, Nak Yong; Hwang, Suk-Seung; Pyun, Jae-Young

doi:10.1109/access.2019.2902564

Cited by 60 publications

(48 citation statements)

References 40 publications

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“…Relative to selecting the maximum, [15] selected the least variance RSSIs over time, arguing that the normal variances are not dramatic. Based on dichotomy, Study [16] and [17] propose an RSSI classification to distinguish singular RSSIs from normal path-loss RSSIs. Paper [17] proposes a k-means clustering algorithm tracing the rating.…”

Section: Rssi Filtering Technologiesmentioning

confidence: 99%

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An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Shi

Liu

et al. 2020

Sensors

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Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.

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Section: Rssi Filtering Technologiesmentioning

confidence: 99%

“…Based on dichotomy, Study [16] and [17] propose an RSSI classification to distinguish singular RSSIs from normal path-loss RSSIs. Paper [17] proposes a k-means clustering algorithm tracing the rating.…”

Section: Rssi Filtering Technologiesmentioning

confidence: 99%

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Shi

Liu

et al. 2020

Sensors

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“…During the offline stage, the RSS fingerprints dataset mapping relationship between signal fingerprints and spatial locations is established. Next in the online stage, estimating location by matching the online RSS fingerprint collection with the offline fingerprint dataset (Du et al, 2107;Subedi et al, 2019). However, the RSS-based method faces many challenges: (1) Fingerprint ambiguity due to the multi-path reflection of RSS in indoor complex environment, which result two different locations may have similar RSS fingerprints and low positioning accuracy .…”

Section: Instructionmentioning

confidence: 99%

COLLABORATIVE Wi-Fi FINGERPRINTING INDOOR POSITIONING USING NEAR RELATION

Wang¹,

Wang²,

Li³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity; (2) labor-intensive of fingerprint collection; (3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8 m can be achieved with the near relation under 1 m interpolation density.

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“…Therefore, RSSI can be used for coarse-grained location estimates. This RF behavior can be represented in a propagation model [31] given by…”

Section: Bluetooth Low Energy (Ble) Based Fingerprintingmentioning

confidence: 99%

A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Gang

Pyun

2019

Energies

Self Cite

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As smartphone built-in sensors, wireless technologies, and processor computing power become more advanced and global positioning system (GPS)-based positioning technologies are improving, location-based services (LBS) have become a part of our daily lives. At the same time, demand has grown for LBS applications in indoor environments, such as indoor path finding and navigation, marketing, entertainment, and location-based information retrieval. In this paper, we demonstrate the design and implementation of a smartphone-based indoor LBS system for location services consisting of smartphone applications and a server. The proposed indoor LBS system uses hybrid indoor positioning methods based on Bluetooth beacons, Geomagnetic field, Inertial Measurement Unit (IMU) sensors, and smartphone cameras and can be used for three types of indoor LBS applications. The performance of each positioning method demonstrates that our system retains the desired accuracy under experimental conditions. As these results illustrate that our system can maintain positioning accuracy to within 2 m 80% of the time, we believe our system can be a real solution for various indoor positioning service needs.

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Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

Cited by 60 publications

References 40 publications

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

COLLABORATIVE Wi-Fi FINGERPRINTING INDOOR POSITIONING USING NEAR RELATION

A Smartphone Indoor Positioning System Using Hybrid Localization Technology

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