An optimized fingerprint positioning algorithm for underground garage environment

Lin, Hailong; Chen, Lei

doi:10.1109/icoin.2016.7427079

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The k-Medoids method is widely used, given its capability to detect and exclude outliers [37]. For this reason, it is used to divide the Wi-Fi fingerprinting data set, providing a better data set partition, and a more accurate cluster centroid selection [38] than k-Means.…”

Section: Related Workmentioning

confidence: 99%

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Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

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Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.

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Section: Related Workmentioning

confidence: 99%

“…2) k-Medoids: k-Medoids is a variant of k-Means, which is more robust to noisy samples (outliers) [38], and uses a representative fingerprint of the cluster (sample medoid) instead of the centroid (averaged sample) [37]. Both, k-Means and k-Medoids, have the same input and output parameters.…”

Section: A Implemented Clustering Models For Fingerprintingmentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

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“…FCM allows a fingerprint to belong to multiple clusters [10]- [12], giving rise to overlapped areas. K-Medoids provides a dataset partition and a cluster selection suitable for fingerprinting [13], being able to detect/exclude outliers [14].…”

Section: Related Workmentioning

confidence: 99%

Accurate and Efficient Wi-Fi Fingerprinting-Based Indoor Positioning in Large Areas

Ramires¹,

Torres-Sospedra

Moreira

2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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The core of fingerprinting is based on the uniqueness of the RF signature in a given location over time. In the offline phase, the fingerprints -the set of RSSI values from different anchors-are collected at given locations generating a radio map. In the online phase, a matching algorithm retrieves the most similar fingerprints from the radio map and computes the position estimate for every operational fingerprint. However, computing the similarities to all the samples in the radio map may be inefficient and not scale in those cases where the radio map is large. Previous attempts to alleviate the computational load rely on the segmentation of the radio map through smart clustering in the offline stage, and a two-step estimation process in the online stage. However, most of the clustering models applied are generic without any consideration about signal propagation and relevant fingerprints are often filtered, resulting in a higher positioning error. This paper introduces Strongest AP Set (SAS), a clustering model conceived for RSSI-based fingerprinting. The results show that SAS is not only able to reduce the computational cost, but also to provide better accuracy than the full model without clustering.

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“…(10,11) Lin et al used an optimized fingerprint-based positioning algorithm to achieve a positioning accuracy of higher than 3.5 m in a garage. (12) In this work, we optimize the general radio-propagation-model-based localization algorithm to achieve an accuracy of higher than 4 m. On the other hand, an improved general regression neural network (GRNN) positioning method based on an optimized method is proposed to make the positioning accuracy higher than 2.4 m.…”

Section: Introductionmentioning

confidence: 99%

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

Xu¹,

Wang²,

Zhang³

et al. 2019

Sensors and Materials

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Keywords: received signal strength indicator (RSSI), ZigBee, localization, filter, maximum likelihood estimation (MLE), general regression neural network (GRNN)Nowadays, indoor positioning is becoming one of the most important issues in smart cities. With the rapid progress of wireless communication and digital electronic technology, wireless sensor networks (WSNs) have been developed and are playing an important role in indoor positioning systems. The received signal strength indicator (RSSI) is adopted by most rangebased localization algorithms. However, the positioning system based on the RSSI is vulnerable to environmental interference and the RSS itself is unstable. To tackle this problem, we propose an improved indoor localization based on the RSSI and general regression neural network (GRNN). In the raw data processing module, an improved average filter is proposed to make the raw data stable and reliable. Then, an improved weighted centroid localization algorithm (IWCLA) is proposed to revise the positioning result on the basis of maximum likelihood estimation (MLE). In the view of the complex and changeable indoor environment, an improved GRNN localization algorithm is proposed to achieve better applicability and higher positioning accuracy. The effectiveness of the proposed methods is verified in different cases through simulation and experiment studies. . Her current research interests include network-based control systems, multiagent systems, and complex networks.

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An optimized fingerprint positioning algorithm for underground garage environment

Cited by 6 publications

References 7 publications

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Accurate and Efficient Wi-Fi Fingerprinting-Based Indoor Positioning in Large Areas

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

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