Bluetooth indoor positioning combination algorithm based on fingerprint matching technology

2021 ◽  
Author(s):  
Qinyang Luo ◽  
Junjie Yang ◽  
Zhicheng Sui ◽  
Qing Ai
Keyword(s):  
Indoor Positioning ◽  
Fingerprint Matching

scholarly journals INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

2018 ◽  
Vol XLII-4 ◽  
pp. 253-260
Author(s):  
E. Gulo ◽  
G. Sohn ◽  
A. Afnan
Keyword(s):  
Mobile Devices ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Viable Approach ◽  
Similar Accuracy ◽  
Pedestrian Dead Reckoning

<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

scholarly journals A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3885 ◽  
Author(s):  
Shuai Zhang ◽  
Jiming Guo ◽  
Nianxue Luo ◽  
Di Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Indoor Positioning ◽  
Test Point ◽  
Reference Sequence ◽  
Fingerprint Matching ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational Coefficient ◽  
Grey Relational

The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used.

scholarly journals Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

scholarly journals PDR Combined with Magnetic Fingerprint Algorithm for Indoor Positioning

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
He Huang ◽  
Kaiyue Qiu ◽  
Wei Li ◽  
Dean Luo
Keyword(s):  
Relative Position ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Fingerprint Matching ◽  
Localization Algorithm ◽  
Position Information ◽  
Matching Algorithm ◽  
Advantages And Disadvantages ◽  
Positioning Method ◽  
Particle Filter Algorithm

Geomagnetism has become a popular technology for indoor positioning, and its accuracy mainly depends on the accuracy of the geomagnetic matching algorithm. Pedestrian dead reckoning technology can calculate the relative position of pedestrians based on sensor information, but only obtain relative position information. According to the advantages and disadvantages of these two techniques, a high-precision GPDR indoor positioning method is proposed, and the improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint fuzzy solution. Finally, a simulation experiment was conducted. The experimental results show that the accuracy of the proposed fusion localization algorithm is 42% higher than that of the PDR algorithm. Compared with a single geomagnetic fingerprint matching algorithm, the positioning accuracy is improved by 57%.

scholarly journals COLLABORATIVE Wi-Fi FINGERPRINTING INDOOR POSITIONING USING NEAR RELATION

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 375-380
Author(s):  
Y. Wang ◽  
W. Wang ◽  
X. Li ◽  
W. Zhang ◽  
R. Guo
Keyword(s):  
Mobile Computing ◽  
Interpolation Method ◽  
Indoor Positioning ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Additional Equipment ◽  
Positioning Method ◽  
Low Efficiency

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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