Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies,...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 05732naaaa2201657uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_76759 | ||
| 005 | 20220111 | ||
| 020 | |a books978-3-0365-1484-0 | ||
| 020 | |a 9783036514833 | ||
| 020 | |a 9783036514840 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-1484-0 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a TB |2 bicssc | |
| 100 | 1 | |a Simon, Gyula |4 edt | |
| 700 | 1 | |a Sujbert, László |4 edt | |
| 700 | 1 | |a Simon, Gyula |4 oth | |
| 700 | 1 | |a Sujbert, László |4 oth | |
| 245 | 1 | 0 | |a Recent Advances in Indoor Localization Systems and Technologies |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (502 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a filter | ||
| 653 | |a finite memory structure | ||
| 653 | |a infinite memory structure | ||
| 653 | |a smoother | ||
| 653 | |a target tracking | ||
| 653 | |a Indoor Positioning System | ||
| 653 | |a WLAN | ||
| 653 | |a C-Means | ||
| 653 | |a K-Means | ||
| 653 | |a Access Point Selection | ||
| 653 | |a RSS-fingerprint | ||
| 653 | |a smartphone | ||
| 653 | |a pedestrian dead reckoning | ||
| 653 | |a heading estimation | ||
| 653 | |a autoregressive model | ||
| 653 | |a adaptive Kalman filter | ||
| 653 | |a indoor localization | ||
| 653 | |a Wi-Fi received signal strength indicator (RSSI) | ||
| 653 | |a semisupervised learning | ||
| 653 | |a feature extraction | ||
| 653 | |a mobile fingerprinting | ||
| 653 | |a trajectory learning | ||
| 653 | |a localization | ||
| 653 | |a hybrid localization | ||
| 653 | |a Bluetooth Low Energy | ||
| 653 | |a extended kalman filter | ||
| 653 | |a internet of things | ||
| 653 | |a proximity sensors | ||
| 653 | |a smartphone sensors | ||
| 653 | |a pedestrian dead reckoning (PDR) | ||
| 653 | |a Wi-Fi indoor positioning | ||
| 653 | |a sensor fusion frameworks | ||
| 653 | |a Kalman filter | ||
| 653 | |a location fingerprinting | ||
| 653 | |a trilateration | ||
| 653 | |a received signal strength indication (RSSI) | ||
| 653 | |a indoor positioning | ||
| 653 | |a 5G system | ||
| 653 | |a hybrid positioning | ||
| 653 | |a geometric dilution of precision | ||
| 653 | |a closed-form solution | ||
| 653 | |a Cramer-Rao lower bound | ||
| 653 | |a visually impaired (VI) | ||
| 653 | |a computer vision | ||
| 653 | |a deep learning | ||
| 653 | |a multi-label convolutional support vector machine (M-CSVM) | ||
| 653 | |a assistive technology | ||
| 653 | |a visually impaired | ||
| 653 | |a navigational system | ||
| 653 | |a indoor navigation | ||
| 653 | |a markers | ||
| 653 | |a mobile robots | ||
| 653 | |a wireless sensor network | ||
| 653 | |a time of arrival (TOA) | ||
| 653 | |a NLOS | ||
| 653 | |a modified probabilistic data association (MPDA) | ||
| 653 | |a indoor location recognition | ||
| 653 | |a received signal strength (RSS) | ||
| 653 | |a Wi-Fi fingerprint positioning | ||
| 653 | |a deep neural network (DNN) | ||
| 653 | |a optimization methods | ||
| 653 | |a adaptive filter | ||
| 653 | |a hidden Markov models (HMM) | ||
| 653 | |a I/O detection | ||
| 653 | |a GPS signal | ||
| 653 | |a machine learning | ||
| 653 | |a positioning applications. | ||
| 653 | |a PDR | ||
| 653 | |a geomagnetic positioning | ||
| 653 | |a particle filter | ||
| 653 | |a genetic algorithm | ||
| 653 | |a Wi-Fi fine timing measurement | ||
| 653 | |a NLOS identification | ||
| 653 | |a Gaussian model | ||
| 653 | |a carrier phase | ||
| 653 | |a differential pseudolite system | ||
| 653 | |a extended Kalman filter | ||
| 653 | |a reliability | ||
| 653 | |a integrity monitoring | ||
| 653 | |a transparent obstacle recognition | ||
| 653 | |a reflection noise | ||
| 653 | |a laser range finder | ||
| 653 | |a path planning | ||
| 653 | |a mobile robot | ||
| 653 | |a automated data acquisition | ||
| 653 | |a remote sensing technologies | ||
| 653 | |a automated progress reporting | ||
| 653 | |a data fusion | ||
| 653 | |a tracking resources | ||
| 653 | |a bearing estimation | ||
| 653 | |a azimuth estimation | ||
| 653 | |a signal processing | ||
| 653 | |a position estimation | ||
| 653 | |a photodiode array | ||
| 653 | |a indoor ranging algorithm | ||
| 653 | |a channel state information | ||
| 653 | |a received signal strength indicator | ||
| 653 | |a VPR | ||
| 653 | |a fusion navigation | ||
| 653 | |a UWB | ||
| 653 | |a multi-path detection | ||
| 653 | |a NLOS and MP discrimination | ||
| 653 | |a SVM | ||
| 653 | |a random forest | ||
| 653 | |a multilayer perceptron | ||
| 653 | |a LOS | ||
| 653 | |a DWM1000 | ||
| 653 | |a fingerprinting | ||
| 653 | |a smart buildings | ||
| 653 | |a mobile devices | ||
| 653 | |a indoor localization technologies | ||
| 653 | |a model based techniques | ||
| 653 | |a quality control | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4208 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76759 |7 0 |z DOAB: description of the publication |