STEPS: An Indoor Navigation Framework for Mobile Devices

Landau, Yael; Ben‐Moshe, Boaz

doi:10.3390/s20143929

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…The most commonly used estimation method is the K-Nearest Neighbor (KNN). More complicated methods include support vector machine (SVM), deep neural networks (DNN), the hidden markov model (HMM), and Gaussian Process Assisted are also implemented [2,[22][23][24][25]. Now, let us formalize the methods using a mathematical model.…”

Section: Research Methods 21 Wifi Fingerprint Positioningmentioning

confidence: 99%

Ensemble learning model for Wifi indoor positioning systems

Tinh

Ngoc

2021

IJ-AI

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<p>WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for each position prediction model. The experimental result shows that our proposed ensemble model outperforms other compared models.</p>

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Section: Research Methods 21 Wifi Fingerprint Positioningmentioning

confidence: 99%

Ensemble learning model for Wifi indoor positioning systems

Tinh

Ngoc

2021

IJ-AI

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“…In more recent literature, it has been more common to use the extended Kalman filter (EKF) and unscented Kalman filter (UKF) approaches, which are suitable for non-linear data, such as the work conducted in [60], which also used IMU sensors along with ultra-wide-band (UWB) technology to perform indoor position and navigation. Moreover, Particle Filtering (PF) [61] has also been used and shown to handle non-linear smartphone sensor data to provide high-accuracy indoor pedestrian positioning. On another note, IMU sensors are not always necessarily located on the user's smartphone, as demonstrated in [62], where the authors used foot-mounted IMU sensors to conduct pedestrian positioning.…”

Section: Inertial Self-localizationmentioning

confidence: 99%

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Dahlke,

Drakoulis,

Fernández García

et al. 2024

Sensors

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In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

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“…Resampling plays a significant role in a PF. The purpose of resampling is to select particles with higher grades [36]. However, the common problem of the resampling process is particle degradation.…”

Section: B Particle Degradation Problemmentioning

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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STEPS: An Indoor Navigation Framework for Mobile Devices

Cited by 7 publications

References 34 publications

Ensemble learning model for Wifi indoor positioning systems

Ensemble learning model for Wifi indoor positioning systems

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Floor Map-Aware Particle Filtering Based Indoor Navigation System

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