Reprint
Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities
Edited by
March 2021
266 pages
- ISBN978-3-0365-0122-2 (Hardback)
- ISBN978-3-0365-0123-9 (PDF)
This is a Reprint of the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary
The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
clustering; data fusion; target detection; Grey Wolf Optimizer; Fireworks Algorithm; hybrid algorithm; exploitation and exploration; GNSS; MIMU; odometer; state constraints; simultaneous localization and mapping (SLAM); range-only SLAM; sum of Gaussian (SoG) filter; cooperative approach; automatic fare collection system; passenger flow forecasting; time series decomposition; singular spectrum analysis; ensemble learning; extreme learning machine; wheeled mobile robot; path panning; laser simulator; fuzzy logic; laser range finder; Wi-Fi camera; sensor fusion; local map; odometry; deep learning; softmax; decision-making; classification; sensor data; Internet of Things; extended target tracking; gamma-Gaussian-inverse Wishart; Poisson multi-Bernoulli mixture; 5G IoT; indoor positioning; deep learning; tracking; localization; navigation; positioning accuracy; single access point positioning; Internet of Things; fuzzy inference; calibration; car-following; Takagi–Sugeno; Kalman filter; microscopic traffic model; continuous-time model; LoRa; localization; positioning; LoRaWAN; TDoA; tracking; map matching; compass; sensor fusion; automotive LFMCW radar; radial velocity; lateral velocity; Doppler-frequency estimation; waveform; signal model; tensor modeling; smart community system; Internet of Things; power efficiency; object-detection coprocessor; histogram of oriented gradient; support vector machine; block-level once sliding detection window; multi-shape detection-window