RSS Based Localization Using a New WKNN Approach

Gholoobi, Amin; Stavrou, Stavros

doi:10.1109/cicsyn.2015.15

Cited by 23 publications

(2 citation statements)

References 17 publications

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“…The RSSI values and RSSI difference (

RSSI) are used as the input of the RBFNN, and the positioning result was obtained. Gholoobi et al [ 11 ] use the weighted k -nearest neighbor (WKNN) method to process the captured signal and achieve indoor localization. Mazan et al [ 12 ] design a feed-forward artificial neural network (ANN) to process data and produce estimated coordinates that denote the position of the user.…”

Section: Introductionmentioning

confidence: 99%

GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM

Liu

Zhong

2018

Sensors

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Abstract:With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency, and their environmental robustness is not strong enough. In this paper, we have introduced an RFID positioning algorithm based on the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM), which is called the GSOS-ELM algorithm. The GSOS-ELM algorithm automatically adjusts the regularization weights of the SOS-ELM algorithm through the GSO algorithm, so that it can quickly obtain the optimal regularization weights under different initial conditions; at the same time, the semi-supervised characteristics of the GSOS-ELM algorithm can significantly reduce the number of labeled reference tags and reduce the cost of positioning systems. In addition, the online learning phase of the GSOS-ELM algorithm can continuously update the system to perceive changes in the environment and resist the environmental interference. We have carried out experiments to study the influence factors and validate the performance, both the simulation and testbed experiment results show that compared with other algorithms, our proposed GSOS-ELM localization system can achieve more accurate positioning results and has certain adaptability to the changes of the environment.

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“…The RSSI values and RSSI difference (

Section: Introductionmentioning

confidence: 99%

GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM

Liu

Zhong

2018

Sensors

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“…However, this model is inaccurate in Bluetooth positioning because of the instable signal strengths. In addition to this method, a series of methods based on nearest neighbor (NN) are proposed, such as K-nearest neighbors (KNN), [11][12][13][14] WKNN, 11,15 and EWKNN (enhanced weighted K-nearest neighbors). 16 These algorithms are efficient in testing phase and have a relative small positioning error.…”

Section: Introductionmentioning

confidence: 99%

Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift

Wang

Sun

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

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Bluetooth positioning is an important and challenging topic in indoor positioning. Although a lot of algorithms have been proposed for this problem, it is still not solved perfectly because of the instable signal strengths of Bluetooth. To improve the performance of Bluetooth positioning, this article proposes a coarse-to-fine positioning method based on weighted K-nearest neighbors and adaptive bandwidth mean shift. The method first employs weighted K-nearest neighbors to generate multi-candidate locations. Then, the testing position is obtained by applying adaptive bandwidth mean shift to the multi-candidate locations, which is used to search for the maximum density of the candidate locations. Experimental result indicates that the proposed method improves the performance of Bluetooth positioning.

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An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

Qiu

Liu

2017

Wireless Pers Commun

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RSS Based Localization Using a New WKNN Approach

Cited by 23 publications

References 17 publications

GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM

GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM

Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift

An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

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