A segmentation-based Radio Tomographic Imaging approach for interference reduction in hostile industrial environments

Koster, Volker; Lewandowski, Andreas; Wietfeld, Christian

doi:10.1109/plans.2012.6236850

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Due to the comparable performance of this method, RTI-based device-free localization has been extensively researched. An RTI-based device-free localization using segmentation algorithm and connected component label algorithm for target tracking was proposed in [19]. Nannuru et al [20] developed a model for multitarget tracking using RTI in indoor environments and successfully tracked three targets with the model.…”

Section: Related Workmentioning

confidence: 99%

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Sun

Zhang

Wang

et al. 2018

Wireless Communications and Mobile Computing

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Currently, localization has been one of the research hot spots in Wireless Sensors Networks (WSNs). However, most localization methods focus on the device-based localization, which locates targets with terminal devices. This is not suitable for the application scenarios like the elder monitoring, life detection, and so on. In this paper, we propose a device-free wireless localization system using Artificial Neural Networks (ANNs). The system consists of two phases. In the off-line training phase, Received Signal Strength (RSS) difference matrices between the RSS matrices collected when the monitoring area is vacant and with a professional in the area are calculated. Some RSS difference values in the RSS difference matrices are selected. The RSS difference values and corresponding matrix indices are taken as the inputs of an ANN model and the known location coordinates are its outputs. Then a nonlinear function between the inputs and outputs can be approximated through training the ANN model. In the on-line localization phase, when a target is in the monitoring area, the RSS difference values and their matrix indices can be obtained and input into the trained ANN model, and then the localization coordinates can be computed. We verify the proposed device-free localization system with a WSN platform. The experimental results show that our proposed device-free wireless localization system is able to achieve a comparable localization performance without any terminal device.

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Section: Related Workmentioning

confidence: 99%

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Sun

Zhang

Wang

et al. 2018

Wireless Communications and Mobile Computing

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“…Other possibility that does not impact the convergence time is the use of different MAC layers, like the improved MAC proposed by Kobatake et al [ 16 ], where they proposed a MCR-SS-CSMA/CA alternative for reducing interferences. Another possibility in this context is the contribution of Koster et al [ 17 ], where they developed a method for reducing the interferences in hostile industrial environments, which could be also applied in other environments.…”

Section: Darp Overviewmentioning

confidence: 99%

Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)

Estevez

González

Glösekötter

et al. 2017

Sensors

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The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios.

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“…The range bins containing the detected target from each CAF are connected together along with a time stamp to form a real time plot of Doppler versus time which can be used for activity classification that is out of the scaope of this paper. The methods in [19] and [20] can be applied to enhance the sharpness of this Doppler record by jointly using the backward and forward detection information.…”

Section: Figure 1 Bistatic Doppler Generationmentioning

confidence: 99%

Indoor target tracking using high doppler resolution passive Wi-Fi radar

Chen

Tan

Woodbridge

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed.

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A segmentation-based Radio Tomographic Imaging approach for interference reduction in hostile industrial environments

Cited by 15 publications

References 4 publications

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)

Indoor target tracking using high doppler resolution passive Wi-Fi radar

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