An advanced fingerprint-based indoor localization scheme for WSNs

Wang, Xizhe; Qiu, Jian; Ye, Sheng; Dai, Guojun

doi:10.1109/iciea.2014.6931530

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…In this study, we used the path‐loss model to simulate and verify our proposed algorithm (DH‐KNN). It is defined as

P_{l} false(d false) false(dB false) = P_{l} false(d_{0} false) + 10 n lg false(d false/ d_{0} false)

where P l ( d ) is the path loss, P l ( d 0 ) represents the path loss at a distance of 1 m, n is the power decay index, and d is the distance between the transmitter and the receiver.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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Dynamic threshold location algorithm based on fingerprinting method

Ding

Wang

2018

ETRI Journal

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The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.

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“…In this study, we used the path‐loss model to simulate and verify our proposed algorithm (DH‐KNN). It is defined as

P_{l} false(d false) false(dB false) = P_{l} false(d_{0} false) + 10 n lg false(d false/ d_{0} false)

where P l ( d ) is the path loss, P l ( d 0 ) represents the path loss at a distance of 1 m, n is the power decay index, and d is the distance between the transmitter and the receiver.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…In this study, we used the path-loss model [10,11] to simulate and verify our proposed algorithm (DH-KNN). It is defined as…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Dynamic threshold location algorithm based on fingerprinting method

Ding

Wang

2018

ETRI Journal

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“…Fingerprinting localization is used in different kinds of localization systems with different hardware, such as UWB [1], RFID [2], WIFI [3], and WSN [4]. Two main aspects of fingerprinting localization are the construction of database and the training of matching model.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, in [1][2][3][4][5][6], there has been research working on the improvement of fingerprinting localization techniques. Fingerprinting localization is used in different kinds of localization systems with different hardware, such as UWB [1], RFID [2], WIFI [3], and WSN [4].…”

Section: Related Workmentioning

confidence: 99%

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Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology

Luo

Gao

2016

International Journal of Distributed Sensor Networks

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With the increasing requirement of localization services in indoor environment, indoor localization techniques have drawn a lot of attention. In recent years, fingerprinting localization techniques have been proved to be effective in indoor localization tasks. Due to the complexity and variability of indoor environment, some traditional geometric localization techniques based on time of arrival (TOA), received signal strength (RSS), or direction of arrival (DOA) may cause big position errors. Unlike common geometric localization methods, fingerprinting localization techniques estimate the position of target by creating a pattern matching model or regression model for the measurement. Therefore, a suitable learning model is the key of a fingerprinting location system. This paper presents a fingerprinting based localization technique using deep belief network (DBN) and ultrawideband (UWB) signals in an office environment. Some location-dependent parameters extracted from channel impulse response (CIR) are used as signatures to build the fingerprinting database. The construction of DBN which is based on the fingerprinting database is also discussed in this paper. Experiment results show that, with appropriate fingerprinting database and model structure, the location system can get desired accuracy.

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Indoor Localization via Candidate Fingerprints and Genetic Algorithm

Song

Huang

et al. 2015

Combinatorial Optimization and Applications

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An advanced fingerprint-based indoor localization scheme for WSNs

Cited by 10 publications

References 12 publications

Dynamic threshold location algorithm based on fingerprinting method

Dynamic threshold location algorithm based on fingerprinting method

Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology

Indoor Localization via Candidate Fingerprints and Genetic Algorithm

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