Lightweight Indoor Localization System

Bâce, Mihai; Pignolet, Yvonne Anne

doi:10.1109/wmnc.2015.22

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The simulation of the k-means algorithm outperforms the EM algorithm in time complexity based on the particle iterations for multiple target tracking. [46] also utilized the k-means to cluster walking trajectory and merge them into a map for lightweight indoor localization. The clustering algorithm reduced the amount of information to be stored in a sensor map.…”

Section: ) Unsupervised K-means Clustering and Position Estimationmentioning

confidence: 99%

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Junoh

Subedi²,

Pyun

2021

IEEE Access

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Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.INDEX TERMS Bluetooth low energy (BLE) beacons, indoor navigation, map constraints, particle filter, unsupervised learning.

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Section: ) Unsupervised K-means Clustering and Position Estimationmentioning

confidence: 99%

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Junoh

Subedi²,

Pyun

2021

IEEE Access

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“…In [ 28 ], a fast path-based fingerprint collection mechanism for site survey was proposed. In [ 29 ], an indoor localization system that captures user-generated walking trajectories augmented with sensor readings, clusters them and merges them into a map was proposed. On the other hand, alternative techniques to supervised learning have been explored.…”

Section: Introductionmentioning

confidence: 99%

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

Herrera-Rivas¹,

Torres-Huitzil

Marin-Castro

et al. 2018

Sensors

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Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m.

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