Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning

2011 ◽  
Vol 204-210 ◽  
pp. 1599-1602 ◽  
Author(s):  
Zhi An Deng ◽  
Yu Bin Xu ◽  
Di Wu
Keyword(s):  
Support Vector Regression ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Physical Space ◽  
Mapping Function ◽  
Local Area ◽  
Indoor Positioning ◽  
Support Vector ◽  
Area Network ◽  
Space Partitioning

Indoor positioning system in wireless local area network (WLAN) has been a subject of intensive research due to its cost effectiveness and reasonable positioning accuracy. A new WLAN indoor positioning algorithm based on support vector regression (SVR) and space partitioning is proposed. The whole positioning environment is partitioned into several subspaces by combining k-means clustering method and binary support vector classifiers (SVC). Then the mapping function between received signal strength (RSS) and the physical space is established by SVR machine for each subspace. Subspace with much smaller physical range means more compact input feature space and leads to the enhancement of generalization capability for each SVR machine. The proposed algorithm and other well-known positioning algorithms are carried and compared in a real WLAN environment. Experimental results show that the proposed algorithm achieves 14.6 percent (0.31m) improvement than the single SVR algorithm in the sense of mean positioning error.

scholarly journals Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2433 ◽  
Author(s):  
Litao Han ◽  
Li Jiang ◽  
Qiaoli Kong ◽  
Ji Wang ◽  
Aiguo Zhang ◽  
...  
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Media Access Control ◽  
Area Network ◽  
Media Access ◽  
Novel Approach ◽  
The Difference

For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings.

scholarly journals Indoor Localization Based on Optimized KNN

2020 ◽  
Vol 5 (2) ◽  
pp. 34
Author(s):  
Xuanyu Zhu
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Area Network ◽  
Online Matching ◽  
Fingerprint Database ◽  
The Cost ◽  
Real Time Information

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.

scholarly journals INVESTIGATION OF BLUETOOTH LOW ENERGY (BLE) AND WIRELESS LOCAL AREA NETWORK (WLAN)-DERIVED DISTANCE MEASUREMENTS USING MOBILE PHONES FOR INDOOR POSITIONING

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 117-124
Author(s):  
K. M. K. Daray ◽  
P. A. G. Todoc ◽  
C. J. S. Sarmiento
Keyword(s):  
Euclidean Distance ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Maximum Error ◽  
Local Area ◽  
Indoor Positioning ◽  
The Philippines ◽  
Low Energy ◽  
Area Network ◽  
Bluetooth Low Energy

Abstract. The indoor positioning problem is not the unavailability of indoor positioning technology, but the difficulty of arriving at an acceptable compromise of technical constraints like cost, performance, ease of use, and availability of technologies. In a developing country such as the Philippines, these constraints have more weight and can restrict the advancement of indoor positioning.This study investigates the use of Bluetooth Low Energy (BLE) and Wireless Local Area Network (WLAN) in Euclidean distance computation, which implies prospect use for indoor positioning through trilateration. It is a proof-of-concept study that BLE and WLAN, using readily-available services such as Nearby Application Programming Interface (API) and beacon simulators, can be used for indoor positioning. This method offers a better trade-off between cost, power, and accuracy.Nearby API and a regular beacon simulator application were used as beacons. The received signal strength interface (RSSI) was measured and used to calculate the Euclidean distance. From each beacon were calculated four different distances, Nearby yielding a maximum error of 26% and a minimum of 4%. The beacon simulator was less accurate and had a maximum error of 60.5% and a minimum of 4%. This shows that it is possible to calculate Euclidean distance using WLAN and BLE, and that Nearby API, which uses both, was more accurate than the beacon simulator, which used only BLE.

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