Real-time, anchor-free node tracking using ultrawideband range and odometry data

Beck, Brian; Baxley, Robert J.; Kim, Joseph

doi:10.1109/icuwb.2014.6958994

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…In [10], [13], [20], the weighting scheme is simplified by setting a unit value to each measurement when the noise measurement model is not available. This causes robustness issue for localization when the measurement error is significant.…”

Section: A Open Challengesmentioning

confidence: 99%

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Multi-UAV Wireless Positioning using Adaptive Multidimensional Scaling and Extended Kalman Filter

Yuan

Guo

Al‐Rubaye

2022

2022 IEEE Globecom Workshops (GC Wkshps)

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Global Navigation Satellite System (GNSS) signal can be blocked when flight vehicles operate in challenging environments such as indoor or adversarial environments. While multi-UAVs are teamed during flight, cooperative localization becomes available to tackle this challenge. Multidimensional Scaling (MDS) method has been well studied for cooperative localization of Wireless Sensor Network (WSN) based on radio frequency (RF) measurement. When noise RF measurement model is lacking, conventional weighted MDS method represents confidence with the measurements by assigning weights relying on distance information between each pair of nodes. In order to process non-distance RF measurements, we present an improved weighted MDS method which applies a novel weighting scheme. In this article, the proposed method conducts velocity estimation for multi-UAV system based on odometry and Frequency Difference of Arrival (FDOA) measurements. Furthermore, an extended Kalman Filter (EKF) algorithm is applied to refine the initial estimation of the MDS method and derive position estimation. Finally, numerical experiments demonstrate the robustness and accuracy of the adaptive MDS-EKF refinement framework for multi-UAV system localization in an unknown dynamic environment lacking measurement noise information.

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Section: A Open Challengesmentioning

confidence: 99%

“…In this paper, the Range-Odometry based MDS method proposed by [20] has been selected to apply our proposed adaptive weighting scheme. The method is summarized in this section.…”

Section: B Range-odometry Mdsmentioning

confidence: 99%

Multi-UAV Wireless Positioning using Adaptive Multidimensional Scaling and Extended Kalman Filter

Yuan

Guo

Al‐Rubaye

2022

2022 IEEE Globecom Workshops (GC Wkshps)

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“…This approach only needs a small number of assumptions and keeps the reference frame through time steps. Therefore, the authors extended this work to make available a real-time tracking system [260]. In this system, the results show that MDSbased approaches are better than the EKF method.…”

Section: Reference-free Approachmentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

Geok

Aung

et al. 2020

Applied Sciences

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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“…The number of pulses per frame is N s = 20480 and the scan length is N = 1920 samples. Note that the time required to collect samples for a scan is approximately9…”

mentioning

confidence: 99%

“…p l (t) may account for distortions due to antennas, reflections, frequencydependent RCS of the target, and filtering in the receiver front-end 9. The actual scan duration is slightly higher as an inter-frame period is present.…”

mentioning

confidence: 99%