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Palabras clave – provistas por la editorial

dynamic objects identification and localization; laser cluster; radial velocity similarity; Pearson correlation coefficient; particle filter; trilateral indoor positioning; RSSI filter; RSSI classification; stability; accuracy; inertial navigation system; artificial neural network; motion tracking; sensor fusion; indoor navigation system; indoor positioning; indoor navigation; radiating cable; leaky feeder; augmented reality; Bluetooth; indoor positioning system; smart hospital; indoor; positioning; visually impaired; deep learning; multi-layered perceptron; inertial sensor; smartphone; multi-variational message passing (M-VMP); factor graph (FG); second-order Taylor expansion; cooperative localization; joint estimation of position and clock; RTLS; indoor positioning system (IPS); position data; industry 4.0; traceability; product tracking; fingerprinting localization; Bluetooth low energy; Wi-Fi; performance metrics; positioning algorithms; location source optimization; fuzzy comprehensive evaluation; DCPCRLB; UAV; unmanned aerial vehicles; NWPS; indoor positioning systems; GPS denied; GNSS denied; autonomous vehicles; visible light positioning; mobile robot; calibration; appearance-based localization; computer vision; Gaussian processes; manifold learning; robot vision systems; image manifold; descriptor manifold; indoor fingerprinting localization; Gaussian filter; Kalman filter; received signal strength indicator; channel state information; indoor localization; visual-inertial SLAM; constrained optimization; path loss model; particle swarm optimization; beacon; absolute position system; cooperative algorithm; intercepting vehicles; robot framework; UWB sensors; Internet of Things (IoT); wireless sensor network (WSN); switched-beam antenna; electronically steerable parasitic array radiator (ESPAR) antenna; received signal strength (RSS); fingerprinting; down-conversion; GPS; navigation; RF repeaters; up-conversion; n/a