Toward Emergency Indoor Localization: Maximum Correntropy Criterion Based Direction Estimation Algorithm for Mobile TOA Rotation Anchor

Xu, Liyuan; He, Jie; Wang, Peng; Pahlavan, Kaveh; Ning, Huansheng; Wang, Qin

doi:10.1109/access.2018.2850967

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…However, under fire conditions, the original map may be not applicable due to the collapse of walls or structural components under fire conditions. At the same time, the base station for the systems may encounter power failure and is not able to offer continuing support [19,20]. Therefore, a real-time mapping and positioning navigation system may be the best solution currently under fire conditions.…”

Section: Description Of the Robot Systemmentioning

confidence: 99%

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

Feng

Niu

et al. 2019

Sensors

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Due to hot toxic smoke and unknown risks under fire conditions, detection and relevant reconnaissance are significant in avoiding casualties. A fire reconnaissance robot was therefore developed to assist in the problem by offering important fire information to fire fighters. The robot consists of three main systems, a display operating system, video surveillance, and mapping and positioning navigation. Augmented reality (AR) goggle technology with a display operating system was also developed to free fire fighters’ hands, which enables them to focus on rescuing processes and not system operation. Considering smoke disturbance, a thermal imaging video surveillance system was included to extract information from the complicated fire conditions. Meanwhile, a simultaneous localization and mapping (SLAM) technology was adopted to build the map, together with the help of a mapping and positioning navigation system. This can provide a real-time map under the rapidly changing fire conditions to guide the fire fighters to the fire sources or the trapped occupants. Based on our experiments, it was found that all the tested system components work quite well under the fire conditions, while the video surveillance system produces clear images under dense smoke and a high-temperature environment; SLAM shows a high accuracy with an average error of less than 3.43%; the positioning accuracy error is 0.31 m; and the maximum error for the navigation system is 3.48%. The developed fire reconnaissance robot can provide a practically important platform to improve fire rescue efficiency to reduce the fire casualties of fire fighters.

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Section: Description Of the Robot Systemmentioning

confidence: 99%

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

Feng

Niu

et al. 2019

Sensors

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“…[24] has proposed a correntropy based distributed algorithm to improve the robustness of estimation for networks with noisy link. In [25], correntropy has been utilized for emergency indoor localization, and its performance has been compared with that of LMS and LMP.…”

Section: Introductionmentioning

confidence: 99%

A Correntropy Based Algorithm for Robust Localization in Wireless Networks

Sedighizad

Seyfe

Valaee

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Localization in wireless networks is possible by measuring some characteristics of the propagating signal related to the position of the user, which is always corrupted by noise components. In this paper, a correntropy based algorithm is proposed for localization and tracking of a mobile station in wireless networks. The performance of the proposed algorithm is compared with the Least Mean Square (LMS) and Least Mean P-norm (LMP) algorithms, and its preference aspects are discussed. The results show that, using correntropy can bring robustness to localization and improve performance in many realistic scenarios such as fat-tail noise distributions, one of the serious bottlenecks of the next-generation 5G wireless communications systems. In addition, it is shown that, the Gaussian kernel of the correntropy function reduces the sensitivity of the algorithm to the learning rate.

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P2PNavi: A System-Level Algorithmic Solution for Highly Accurate Direction Estimation for Infrastructure-Free Indoor Navigation

Wang

et al. 2021

IEEE Systems Journal

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Toward Emergency Indoor Localization: Maximum Correntropy Criterion Based Direction Estimation Algorithm for Mobile TOA Rotation Anchor

Cited by 6 publications

References 45 publications

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display

A Correntropy Based Algorithm for Robust Localization in Wireless Networks

P2PNavi: A System-Level Algorithmic Solution for Highly Accurate Direction Estimation for Infrastructure-Free Indoor Navigation

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