TDoA and RSS Based Extended Kalman Filter for Indoor Person Localization

Lategahn, Julian; Müller, Marcel; Röhrig, Christof

doi:10.1109/vtcfall.2013.6692433

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…Extended Kalman filter (EKF) is always considered as an alternative method to solve nonlinear filtering problems. For RSS measurements with EKF implementation, relevant research work can be found, for instance, in [48], [49] and [50]. Here in this paper, we also compare the proposed particle filtering algorithm with EKF.…”

Section: Simulated Datamentioning

confidence: 99%

Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning

Zhao

Fritsche

Yin

et al. 2018

IEEE Trans. Veh. Technol.

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The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider positioning of devices based on a time series of proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Motion model will be needed together with the measurements to determine the position of the device. Therefore, sequential Monte Carlo methods, namely particle filtering and smoothing, are applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report, and is further compared to parametric Cramér-Rao lower bounds. Finally, the position accuracy is also evaluated with real experimental data.

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Section: Simulated Datamentioning

confidence: 99%

Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning

Zhao

Fritsche

Yin

et al. 2018

IEEE Trans. Veh. Technol.

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“…Moreover, non-line of sight components of the signal are detected by an iterative algorithm which is based on the incoming hybrid signals in [ 17 ]. In [ 18 ], positioning and tracking of people is performed using the extended Kalman filter based on time difference of arrival and AoA. Positioning of people is an important application of WSN and has a vital significance in health care systems [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks

Khan

Salman

Kemp

et al. 2016

Sensors

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Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution.

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“…It is clear that the TDoA results calculated by the proposed method are closer to the theoretical prediction at all the seven positions. Lategahn et al [44] presented a fusion method of RSSI and TDoA, which chose the relatively reliable result as the target position. However, it can be seen that the Lategahn method will not work well in this experiment setup, especially at position #6 and #7, for which neither the RSSI method nor the conventional TDoA method can give a good position estimation.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation

Xie

Jiang

Zhao

et al. 2019

Sensors

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Wi-Fi-based indoor position sensing solutions have the advantages of easy integration in mobile phones and low cost by using existing Wi-Fi access points. The mainstream methods are commonly based on the received signal strength indicator (RSSI), which suffers from multipath interference in complicated indoor environments. Through the in-depth analysis of the multipath interference, an RSSI-assisted time difference of arrival (TDoA) method is proposed for Wi-Fi-based indoor position sensing in this work. The key idea is to compensate for the multipath interference in the received signals based on the coarse estimation using RSSI and TDoA calculation. A prototype system has been implemented to validate the proposed method. Experimental results have demonstrated the effectiveness of the proposed method, especially for handling the multipath interference with small propagation delay difference. Experimental results show that the indoor position sensing system can achieve a 90th percentile error of 0.3 m. The proposed method can also achieve moderate computational complexity and moderate real-time performance compared to other methods.

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TDoA and RSS Based Extended Kalman Filter for Indoor Person Localization

Cited by 18 publications

References 12 publications

Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning

Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning

Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks

A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation

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