A comprehensive approach for optimizing ToA-localization in harsh industrial environments

Lewandowski, Andreas; Wietfeld, Christian

doi:10.1109/plans.2010.5507255

Cited by 20 publications

(8 citation statements)

References 14 publications

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“…According to the acceleration measured by the accelerometer, the acceleration g a of the carrier relative to the geographic coordinate system can be obtained, as shown in equation (5).…”

Section: Data Preprocessingmentioning

confidence: 99%

Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method

Liu¹,

Li²,

Wang³

et al. 2019

Proceedings of the 12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019,

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Using Time Difference of Arrival(TDoA) positioning results and the Inertial measurement unit(IMU) for calculating the motion state of information fusion can significantly improve the positioning accuracy, due to the carrier in the process of movement, the state of the system noise and measurement noise are not strictly obey the normal gaussian distribution, which makes the traditional fusion positioning method using Kalman Filtering algorithm less accurate. This paper proposes an adaptive filter fusion localization mechanism with LSTM network correction. Firstly, a data preprocessing method is designed to convert IMU data from the carrier coordinate system to the geographical coordinate system. Then, based on kinematics theory, the state equation and measurement equation of Adaptive Kalman Filter are established and the system state noise is obtained. Furthermore, the model adaptively to update the carrier coordinate, system state noise and measurement noise. Finally, the carrier trajectory coordinates predicted by the coupled LSTM model are used to obtain the final positioning results and complete the carrier trajectory filtering. Experimental results show that the proposed fusion localization mechanism can effectively improve the accuracy of carrier trajectory localization.

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“…According to the acceleration measured by the accelerometer, the acceleration g a of the carrier relative to the geographic coordinate system can be obtained, as shown in equation (5).…”

Section: Data Preprocessingmentioning

confidence: 99%

Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method

Liu¹,

Li²,

Wang³

et al. 2019

Proceedings of the 12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019,

View full text Add to dashboard Cite

show abstract

“…In (18), the coordinates and are obtained by the least square method and the least squares regression model [21,22], while the estimation function is defined by…”

Section: Attribute Set Selectionmentioning

confidence: 99%

Research on Secure Localization Model Based on Trust Valuation in Wireless Sensor Networks

Qian

et al. 2017

Security and Communication Networks

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Secure localization has become very important in wireless sensor networks. However, the conventional secure localization algorithms used in wireless sensor networks cannot deal with internal attacks and cannot identify malicious nodes. In this paper, a localization based on trust valuation, which can overcome a various attack types, such as spoofing attacks and Sybil attacks, is presented. The trust valuation is obtained via selection of the property set, which includes estimated distance, localization performance, position information of beacon nodes, and transmission time, and discussion of the threshold in the property set. In addition, the robustness of the proposed model is verified by analysis of attack intensity, localization error, and trust relationship for three typical scenes. The experimental results have shown that the proposed model is superior to the traditional secure localization models in terms of malicious nodes identification and performance improvement.

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“…Three‐dimensional indoor positioning is complex and requires a combination of technologies. In , a three‐dimensional algorithm is presented combining RSSI, time of arrival and sophisticated three‐dimensional ray tracing. Ray tracing, which is a widely accepted technique for genuine‐three‐dimensional positioning, is based on geometrical optics.…”

Section: Related Workmentioning

confidence: 99%

Pseudo‐3D RSSI‐based WSN localization algorithm using linear regression

Vanheel

Verhaevert

Laermans

et al. 2013

Wireless Communications

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Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location-aware applications. For a final breakthrough these systems need fast deployment and easy auto-configuration. In this study, we use the real-life iMinds test bed to expand a two-dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three-dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non-parametric statistical tests, we show that our previously designed twodimensional preprocessing performs equally well in pseudo-three dimensions: the preprocessing reduces the position error in a statistically significant way.

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A comprehensive approach for optimizing ToA-localization in harsh industrial environments

Cited by 20 publications

References 14 publications

Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method

Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method

Research on Secure Localization Model Based on Trust Valuation in Wireless Sensor Networks

Pseudo‐3D RSSI‐based WSN localization algorithm using linear regression

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