Toward Scalable and Robust Indoor Tracking: Design, Implementation, and Evaluation

Jin, Feiyu; Li, Kai; Zhang, Hao; Ng, Joseph Kee‐Yin; Guo, Songtao; Lee, Victor C. S.; Son, Sang H.

doi:10.1109/jiot.2019.2953376

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…In Table 1 , the values of coverage are reported in meters. Scalability indicates the possibility with which the technology can be extended, referring both to the coverage area and to the number of users supported simultaneously [ 20 ]. Security and privacy represent the level of control of access to the subjects’ personal information.…”

Section: Ipsmentioning

confidence: 99%

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

Bibbò

Carotenuto

Corte

2022

Sensors

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The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology’s concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

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Section: Ipsmentioning

confidence: 99%

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

Bibbò

Carotenuto

Corte

2022

Sensors

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“…We analyzed three clustering algorithms to handle the particle clustering (e.g., K-means [40], [41], DBSCAN [18], [42] and Mean-Shift Clustering [43]). Due to the resource constraints of smartphones and the planned usage in real-time for (K): 5.…”

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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“…Therefore, we have to suffice for suboptimal solutions and use the methods developed for non-linear systems. Extended Kalman Filter (EKF) (Kalman and Bucy, 1961;Ristic et al, 2004) has been everybody's go-to option for non-linear systems. It works by linearizing the non-linear state space equations using first-order truncation of Taylor series.…”

Section: • Bearing and Doppler Measurements: Los Angle Must Not Remain Constantmentioning

confidence: 99%

“…Sensors required for avoiding collisions has to be simple, low-powered, and rugged (Li et al, 2019). Moreover, successful utilization of such collision avoidance systems lies in its miniaturization and smooth integration (Jin et al, 2020). Currently, a lot of focus is put on collision avoidance using cameras (Quan et al, 2020).…”

mentioning

confidence: 99%

Comparative study of Kalman filter-based target motion analysis by incorporating Doppler frequency measurements

Haq

Nasir

Iqbal

et al. 2021

International Journal on Smart Sensing and Intelligent Systems

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Target motion analysis is a key requirement of autonomous and selfdriving machines like drones and robots. However, with strict weight limits, the aerospace industry is always on the hunt for simpler and lighter sensing solutions. Continuous-wave Doppler radars are the simplest radars that can easily obtain a target's relative velocity using the Doppler shift in the received wave. However, these radars cannot provide the target's range. In this work, we address the problem of obtaining target's range and velocity by incorporating Doppler frequency measurements from a simple continuous wave Doppler radar. To this end, we find out the movement patterns and maneuvers that an observer can make to converge to the target's location. After presenting the observability requirements, we design and compare various non-linear Kalman filter-based target trackers. We experimented with different simulation scenarios to compare the tracking results with bearings-only, frequency-only, and bearingsfrequency measurement sets. In our analysis, Unscented Kalman Filter with bearings-frequency measurements performed best. Experiments show that an observer can locate the target accurately within 10 cm by incorporating Doppler frequency measurements. Moreover, it also reduced the convergence time to a fraction of a second.

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Toward Scalable and Robust Indoor Tracking: Design, Implementation, and Evaluation

Cited by 13 publications

References 26 publications

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Comparative study of Kalman filter-based target motion analysis by incorporating Doppler frequency measurements

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