An Efficient Target Tracking in Directional Sensor Networks Using Adapted Unscented Kalman Filter

Izadi-Ghodousi, Zahra; Hosseinpour, Mahsa; Safaei, Farzad; Mohajerzadeh, Amir Hossein; Alishahi, Mohammad

doi:10.1007/s11277-019-06660-z

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Recent works employed UKF for their systems such as F. Tian, et al [3], Z.I. Ghodousi, et al, [4], H. Lu, et al, [5], S. Wang, et al, [6], J. Yan, et al, [7].…”

Section: Introductionmentioning

confidence: 99%

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Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

Adiono

Prasetiyo

2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">In this report, we perform the digital filter computation using Matlab for Wi-Fi tracking application. This work motivates to improve the accuracy of filter algorithm in the RSSI-based distance localization system. There are several aspects that we can improve, e.g., in the Filter part and Path-loss model. But, in this work, we focus on filter part; Unscented Kalman Filter (UKF) is implemented to replace linear Kalman Filter (KF), which is used in previous work. Based on the performance comparison, UKF has 90% hit ratio while linear KF has only 81.15 % hit ratio. We found that UKF can handle the noise in RSSI. Further work, the UKF algorithm is then embedded on the server system.</p>

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“…Recent works employed UKF for their systems such as F. Tian, et al [3], Z.I. Ghodousi, et al, [4], H. Lu, et al, [5], S. Wang, et al, [6], J. Yan, et al, [7].…”

Section: Introductionmentioning

confidence: 99%

“…Based on literature review, including Ref. [3][4][5][6][7], we found that the NLS + UKF technique for RSSI-based Wi-Fi tracking system applications (precise indoor Wi-Fi localization) has not been done by many researchers. This paper is more emphasis on UKF computation in Matlab.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

Adiono

Prasetiyo

2020

Int. J. Interact. Mob. Technol.

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Enhancing Heart Rate Detection Accuracy Through the Kalman Filter Algorithm

Manullang,

Sihombing,

Nasution

2024

2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA)

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A Systematic Review of Location Aware Schemes in the Internet of Things

Khan

Saboor

Kim

et al. 2021

Sensors

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The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

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An Efficient Target Tracking in Directional Sensor Networks Using Adapted Unscented Kalman Filter

Cited by 3 publications

References 43 publications

Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

Enhancing Heart Rate Detection Accuracy Through the Kalman Filter Algorithm

A Systematic Review of Location Aware Schemes in the Internet of Things

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