Robust Wi-Fi based indoor positioning with ensemble learning

Taniuchi, Daisuke; Maekawa, Taira

doi:10.1109/wimob.2014.6962230

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…Lee and Park [19] presented a technique to estimate the robot position in each floor by using gyroscopes to recognize the robot motion status on the stairs. e authors of [20][21][22][23] presented robust and reliability positioning techniques for the IPSs based on Scene Analysis. ey focused on handling the problems of different mobile devices and reducing the impact of environmental dynamics on the accuracy performance.…”

Section: Related Workmentioning

confidence: 99%

“…ey presented the localization algorithm based on the Multipath Component Analysis (MCA) to be robust against changes in the environment. Taniuchi and Maekawa [22] proposed a new Wi-Fi indoor positioning algorithm by which the system can be robust over unstable Wi-Fi APs. eir algorithm was based on the location fingerprinting technique that used the ensemble learning approaches to handle the problems of Wi-Fi positioning caused by unstable and uncontrollable infrastructure such as the movement of people.…”

Section: Related Workmentioning

confidence: 99%

“…According to the literature reviewed on the robust localization process, although some existing works have studied the robust and reliable location problem [18][19][20][21][22][23], they focused on robustness issues, including robust tracking of floor changes when the robots moved in the staircase, improving the robustness of dynamic changes to the indoor environment which lead to the instability of fingerprint information, and reducing the influence of different mobile Single floor and multifloor Yes devices. In particular, they did not consider the robustness in terms of faulty RNs during the online estimation phase.…”

Section: Related Workmentioning

confidence: 99%

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RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Maneerat

Kaemarungsi

2019

Mobile Information Systems

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Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Maneerat

Kaemarungsi

2019

Mobile Information Systems

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“…It was found that AdaBoost and Bagging are the two best classifiers for indoor localization. Taniuchi et al [37] proposed an ensemble learning algorithm to improve the performance of RSS based indoor localization in the WLAN environment. After training several multiple weak learners, a weighted average strategy is adopted to yield the final position estimate.…”

Section: Related Workmentioning

confidence: 99%

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Guo

Ansari

2017

IEEE Trans. Veh. Technol.

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Abstract-Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourthorder cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.Index Terms-AdaBoost, group Of Fingerprints (GOOF), multiple antennas, multiple Classifiers mUltiple Samples (MUCUS) fusion localization, universal software radio peripheral (USRP).

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“…UbiComp '16, September 12-16, 2016 [12,45], Bluetooth [47], and Wi-Fi [25,42]. The recognized context information can be used in real-world services, e.g., context-aware systems, lifelogging, and surveillance of the elderly [18,31].…”

Section: Introductionmentioning

confidence: 99%

Predicting location semantics combining active and passive sensing with environment-independent classifier

Tachikawa

Maekawa

Matsushita

2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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This paper presents a method for estimating a user's indoor location without using training data collected by the user in his/her environment. Specifically, we attempt to predict the user's location semantics, i.e., location classes such as restroom and meeting room. While indoor location information can be used in many real-world services, e.g., context-aware systems, lifelogging, and monitoring the elderly, estimating the location information requires training data collected in an environment of interest. In this study, we combine passive sensing and active sound probing to capture and learn inherent sensor data features for each location class using labeled training data collected in other environments. In addition, this study modifies the random forest algorithm to effectively extract inherent sensor data features for each location class. Our evaluation showed that our method achieved about 85% accuracy without using training data collected in test environments.

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Robust Wi-Fi based indoor positioning with ensemble learning

Cited by 22 publications

References 8 publications

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Predicting location semantics combining active and passive sensing with environment-independent classifier

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