Results 151 to 160 of about 8,425 (174)

A Fault-Tolerant Localization Method for 5G/INS Based on Variational Bayesian Strong Tracking Fusion Filtering with Multilevel Fault Detection. [PDF]

Sensors (Basel)
Deng Z, Ma Z, Luo H, Guo J, Tian Z.
europepmc   +1 more source

Robust Indoor Positioning with Smartphone by Utilizing Encoded Chirp Acoustic Signal. [PDF]

Sensors (Basel)
Cheng B, Huang Y, Zou C.
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Optimization of range based self-localization problem in wireless sensor networks using improved cuckoo search algorithm. [PDF]

Sci Rep
Aouthu S, Dhanalakshmi N, Seshagiri T, Dilli R.   +3 more
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Insulator Partial Discharge Localization Based on Improved Wavelet Packet Threshold Denoising and Gxx-β Generalized Cross-Correlation Algorithm. [PDF]

Sensors (Basel)
Ji H, Tang Z, Zheng C, Liu X, Liu L.
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Multisensory Fusion for Unsupervised Spatiotemporal Speaker Diarization. [PDF]

Sensors (Basel)
Xylogiannis P, Vryzas N, Vrysis L, Dimoulas C.   +3 more
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Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM. [PDF]

Sensors (Basel)
He Z, Lu J, Cui S, Zhou C, Shao Y, Wu Q, Zuo H.   +6 more
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Drone's Angle-of-Arrival Estimation Using a Switched-Beam Antenna and Single-Channel Receiver. [PDF]

Sensors (Basel)
Han S, Jang BJ.
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An auditory-visual cooperative perception method for honking vehicle localization. [PDF]

PLoS One
Yuan F, Kang J, Yin J, Cao J.
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Time-Difference-of-Arrival (TDOA)-Based Distributed Target Localization by A Robotic Network

IEEE Transactions on Control of Network Systems, 2020
Localization and tracking of a moving target arises in many different contexts and is of particular interest in the field of robotic networks. One important class of localization schemes exploits the time-difference-of-arrival (TDOA) of a signal emitted by the target and detected by multiple sensors.
Osama Ennasr, Xiaobo Tan
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Multi-cycle estimator for time-difference-of-arrival (TDOA) and its performance

IEE Proceedings - Radar, Sonar and Navigation, 2006
Single-cycle time-difference-of-arrival estimator utilises signal's cyclostationarity property of only one cycle frequency. A new approach exploiting the signal's cyclostationarity property of more than one cycle frequency is introduced. Compared with the single-cycle estimator, the method can reduce more estimation variance. The qualitative discussion
Z.-T. Huang, Y.-Y. Zhou
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