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Handbook of Position Location : Theory, Practice, and Advances
Handbook of Position Location : Theory, Practice, and Advances
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, New Jersey, : Wiley-IEEE Press, [2019]
Descrizione fisica 1 online resource
Soggetto topico Location-based services
Wireless communication systems
Wireless localization
ISBN 1-119-43461-0
9781119434610
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555068503321
Hoboken, New Jersey, : Wiley-IEEE Press, [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of position location : theory, practice, and advances / / edited by Seyed A. Zekavat, R. Michael Buehrer
Handbook of position location : theory, practice, and advances / / edited by Seyed A. Zekavat, R. Michael Buehrer
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE Press, , 2019
Descrizione fisica 1 online resource
Disciplina 910.285
Soggetto topico Location-based services
Wireless communication systems
Wireless localization
ISBN 1-119-43461-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Fundamentals of Position Location -- Fundamentals of Position Location. Wireless Positioning Systems: Operation, Application, and Comparison / S A (Reza) Zekavat, Stuti Kansal, Allen H Levesque -- Localization Sensor Error Measures and Analysis / Mojtaba Bahramgiri, S A (Reza) Zekavat -- Source Localization: Algorithms and Analysis / H C So -- Channel Modeling and Its Impact on Localization / S A (Reza) Zekavat -- An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications / Shu Ting Goh, S A (Reza) Zekavat, Ossama Abdelkhalik -- TOA and DOA Based Positioning -- TOA and DOA Based Positioning. Fundamentals of Time-of-Arrival-Based Position Location / R Michael Buehrer, Swaroop Venkatesh -- TOA Estimation Techniques: A Comparison / Mohsen Pourkhaatoun, S A (Reza) Zekavat -- Wireless Localization Using Ultra-Wideband Signals / Liuqing Yang, Huilin Xu -- An Introduction to Direction-of-Arrival Estimation Techniques / S A (Reza) Zekavat -- Positioning in Inhomogeneous Media / Mohsen Jamalabdollahi, S A (Reza) Zekavat, Michigan Tech -- Received Signal Strength Based Positioning -- Received Signal Strength Based Positioning. Fundamentals of Received Signal Strength-Based Position Location / Jeong Heon Lee, R Michael Buehrer -- On the Performance of Wireless Indoor Localization Using Received Signal Strength / Jie Yang, Yingying Chen, Richard P Martin, Wade Trappe, Marco Gruteser -- Impact of Anchor Placement and Anchor Selection on Localization Accuracy / Yingying Chen, Jie Yang, Wade Trappe, Richard P Martin -- Kernel Methods for RSS-Based Indoor Localization / Piyush Agrawal, Neal Patwari -- Fingerprinting Location Techniques / Rafael Saraiva Campos, Lisandro Lovisolo -- LOS/NLOS Localization - Identification - Mitigation -- LOS/NLOS Localization - Identification - Mitigation. NLOS Identification and Localization / Wenjie Xu, Zhonghai Wang, S A (Reza) Zekavat -- NLOS Mitigation Methods for Geolocation / Joni Polili Lie, Chin-Heng Lim, Chong-Meng Samson See -- Mobile Position Estimation Using Received Signal Strength and Time of Arrival in Mixed LOS/NLOS Environments / Bamrung Tau Siesku, Feng Zheng, Thomas Kaiser -- Mobile Tracking in Mixed Line-of-Sight/Non-Line-of-Sight Conditions: Algorithms and Theoretical Lower Bound / Liang Chen, Simo Ali-Lo¨ytty, Robert Piche´, Lenan Wu -- Global Positioning -- Global Positioning. Overview of Global Positioning Systems / Fabio Dovis, Davide Margaria, Paolo Mulassano, Fabrizio Dominici -- Digital Signal Processing for GNSS Receivers / Letizia Lo Presti, Maurizio Fantino, Marco Pini -- Kalman Filter-based Approaches for Positioning: Integrating Global Positioning with Inertial Sensors / Emanuela Falletti, Gianluca Falco -- An overview on Global Positioning Techniques for Harsh Environments / Nicola Linty, Fabio Dovis -- Network Localization -- Network Localization. Collaborative Position Location / R Michael Buehrer, Tao Jia -- Polynomial-Based Methods for Localization in Multiagent Systems / Iman Shames, Bariscedil; Fidan, Brian D O Anderson, Hatem Hmam -- Belief Propagation Techniques for Cooperative Localization in Wireless Sensor Networks / Vladimir Savic, Santiago Zazo -- Error Characteristics of AD HOC Positioning Systems / Dragoscedil; Niculescu -- Self-Localization of UAV Formations Using Bearing Measurements / Iman Shames, Baris? Fidan, Brian D O Anderson, Hatem Hmam -- Special Topics and Applications -- Special Topics and Applications. Localization for Autonomous Driving / Ami Woo, Baris Fidan, William W Melek -- RFID-Based Autonomous Mobile Robot Navigation / Sunhong Park, Guillermo Enriquez, Shuji Hashimoto -- Visible Light-Based Communication and Localization / Lisandro Lovisolo, Michel P Tcheou, Fla´vio R Aacute;vila -- Positioning in LTE / Ari Kangas, Iana Siomina, Torbjo¨rn Wigren -- Automated Wildlife Radio Tracking / Robert B MacCurdy, Allert I Bijleveld, Richard M Gabrielson, Kathryn A Cortopassi -- Wireless Local Positioning Systems / S A (Reza) Zekavat -- Near-Ground Channel Modeling with Applications in Wireless Sensor Networks and Autonomous Driving / Amir Torabi, S A (Reza) Zekavat.
Record Nr. UNINA-9910676532603321
Hoboken, New Jersey : , : Wiley-IEEE Press, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to visual SLAM : from theory to practice / / Xiang Gao, Tao Zhang
Introduction to visual SLAM : from theory to practice / / Xiang Gao, Tao Zhang
Autore Gao Xiang, L.L.M.
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (386 pages)
Disciplina 621.38456
Soggetto topico Wireless localization
Computer vision
Sensor networks
ISBN 981-16-4939-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- What is This Book Talking About? -- How to Use This Book? -- Source Code -- Targeted Readers -- Style -- Exercises (Self-test Questions) -- Acknowledgments -- Contents -- Part I Fundamental Knowledge -- 1 Introduction to SLAM -- 1.1 Meet ``Little Carrot'' -- 1.1.1 Monocular Camera -- 1.1.2 Stereo Cameras and RGB-D Cameras -- 1.2 Classical Visual SLAM Framework -- 1.2.1 Visual Odometry -- 1.2.2 Backend Optimization -- 1.2.3 Loop Closing -- 1.2.4 Mapping -- 1.3 Mathematical Formulation of SLAM Problems -- 1.4 Practice: Basics -- 1.4.1 Installing Linux -- 1.4.2 Hello SLAM -- 1.4.3 Use CMake -- 1.4.4 Use Libraries -- 1.4.5 Use IDE -- 2 3D Rigid Body Motion -- 2.1 Rotation Matrix -- 2.1.1 Points, Vectors, and Coordinate Systems -- 2.1.2 Euclidean Transforms Between Coordinate Systems -- 2.1.3 Transform Matrix and Homogeneous Coordinates -- 2.2 Practice: Use Eigen -- 2.3 Rotation Vectors and the Euler Angles -- 2.3.1 Rotation Vectors -- 2.3.2 Euler Angles -- 2.4 Quaternions -- 2.4.1 Quaternion Operations -- 2.4.2 Use Quaternion to Represent a Rotation -- 2.4.3 Conversion of Quaternions to Other Rotation Representations -- 2.5 Affine and Projective Transformation -- 2.6 Practice: Eigen Geometry Module -- 2.6.1 Data Structure of the Eigen Geometry Module -- 2.6.2 Coordinate Transformation Example -- 2.7 Visualization Demo -- 2.7.1 Plotting Trajectory -- 2.7.2 Displaying Camera Pose -- 3 Lie Group and Lie Algebra -- 3.1 Basics of Lie Group and Lie Algebra -- 3.1.1 Group -- 3.1.2 Introduction of the Lie Algebra -- 3.1.3 The Definition of Lie Algebra -- 3.1.4 Lie Algebra mathfrakso(3) -- 3.1.5 Lie Algebra mathfrakse(3) -- 3.2 Exponential and Logarithmic Mapping -- 3.2.1 Exponential Map of SO(3) -- 3.2.2 Exponential Map of SE(3) -- 3.3 Lie Algebra Derivation and Perturbation Model -- 3.3.1 BCH Formula and Its Approximation.
3.3.2 Derivative on SO(3) -- 3.3.3 Derivative Model -- 3.3.4 Perturbation Model -- 3.3.5 Derivative on SE(3) -- 3.4 Practice: Sophus -- 3.4.1 Basic Usage of Sophus -- 3.4.2 Example: Evaluating the Trajectory -- 3.5 Similar Transform Group and Its Lie Algebra -- 3.6 Summary -- 4 Cameras and Images -- 4.1 Pinhole Camera Models -- 4.1.1 Pinhole Camera Geometry -- 4.1.2 Distortion -- 4.1.3 Stereo Cameras -- 4.1.4 RGB-D Cameras -- 4.2 Images -- 4.3 Practice: Images in Computer Vision -- 4.3.1 Basic Usage of OpenCV -- 4.3.2 Basic OpenCV Images Operations -- 4.3.3 Image Undistortion -- 4.4 Practice: 3D Vision -- 4.4.1 Stereo Vision -- 4.4.2 RGB-D Vision -- 5 Nonlinear Optimization -- 5.1 State Estimation -- 5.1.1 From Batch State Estimation to Least-Square -- 5.1.2 Introduction to Least-Squares -- 5.1.3 Example: Batch State Estimation -- 5.2 Nonlinear Least-Square Problem -- 5.2.1 The First and Second-Order Method -- 5.2.2 The Gauss-Newton Method -- 5.2.3 The Levernberg-Marquatdt Method -- 5.2.4 Conclusion -- 5.3 Practice: Curve Fitting -- 5.3.1 Curve Fitting with Gauss-Newton -- 5.3.2 Curve Fitting with Google Ceres -- 5.3.3 Curve Fitting with g2o -- 5.4 Summary -- Part II SLAM Technologies -- 6 Visual Odometry: Part I -- 6.1 Feature Method -- 6.1.1 ORB Feature -- 6.1.2 Feature Matching -- 6.2 Practice: Feature Extraction and Matching -- 6.2.1 ORB Features in OpenCV -- 6.2.2 ORB Features from Scratch -- 6.2.3 Calculate the Camera Motion -- 6.3 2D-2D: Epipolar Geometry -- 6.3.1 Epipolar Constraints -- 6.3.2 Essential Matrix -- 6.3.3 Homography -- 6.4 Practice: Solving Camera Motion with Epipolar Constraints -- 6.4.1 Discussion -- 6.5 Triangulation -- 6.6 Practice: Triangulation -- 6.6.1 Triangulation with OpenCV -- 6.6.2 Discussion -- 6.7 3D-2D PnP -- 6.7.1 Direct Linear Transformation -- 6.7.2 P3P -- 6.7.3 Solve PnP by Minimizing the Reprojection Error.
6.8 Practice: Solving PnP -- 6.8.1 Use EPnP to Solve the Pose -- 6.8.2 Pose Estimation from Scratch -- 6.8.3 Optimization by g2o -- 6.9 3D-3D Iterative Closest Point (ICP) -- 6.9.1 Using Linear Algebra (SVD) -- 6.9.2 Using Non-linear Optimization -- 6.10 Practice: Solving ICP -- 6.10.1 Using SVD -- 6.10.2 Using Non-linear Optimization -- 6.11 Summary -- 7 Visual Odometry: Part II -- 7.1 The Motivation of the Direct Method -- 7.2 2D Optical Flow -- 7.2.1 Lucas-Kanade Optical Flow -- 7.3 Practice: LK Optical Flow -- 7.3.1 LK Flow in OpenCV -- 7.3.2 Optical Flow with Gauss-Newton Method -- 7.3.3 Summary of the Optical Flow Practice -- 7.4 Direct Method -- 7.4.1 Derivation of the Direct Method -- 7.4.2 Discussion of Direct Method -- 7.5 Practice: Direct method -- 7.5.1 Single-Layer Direct Method -- 7.5.2 Multi-layer Direct Method -- 7.5.3 Discussion -- 7.5.4 Advantages and Disadvantages of the Direct Method -- 8 Filters and Optimization Approaches: Part I -- 8.1 Introduction -- 8.1.1 State Estimation from Probabilistic Perspective -- 8.1.2 Linear Systems and the Kalman Filter -- 8.1.3 Nonlinear Systems and the EKF -- 8.1.4 Discussion About KF and EKF -- 8.2 Bundle Adjustment and Graph Optimization -- 8.2.1 The Projection Model and Cost Function -- 8.2.2 Solving Bundle Adjustment -- 8.2.3 Sparsity -- 8.2.4 Minimal Example of BA -- 8.2.5 Schur Trick -- 8.2.6 Robust Kernels -- 8.2.7 Summary -- 8.3 Practice: BA with Ceres -- 8.3.1 BAL Dataset -- 8.3.2 Solving BA in Ceres -- 8.4 Practice: BA with g2o -- 8.5 Summary -- 9 Filters and Optimization Approaches: Part II -- 9.1 Sliding Window Filter and Optimization -- 9.1.1 Controlling the Structure of BA -- 9.1.2 Sliding Window -- 9.2 Pose Graph Optimization -- 9.2.1 Definition of Pose Graph -- 9.2.2 Residuals and Jacobians -- 9.3 Practice: Pose Graph -- 9.3.1 Pose Graph Using g2o Built-in Classes.
9.3.2 Pose Graph Using Sophus -- 9.4 Summary -- 10 Loop Closure -- 10.1 Loop Closure and Detection -- 10.1.1 Why Loop Closure Is Needed -- 10.1.2 How to Close the Loops -- 10.1.3 Precision and Recall -- 10.2 Bag of Words -- 10.3 Train the Dictionary -- 10.3.1 The Structure of Dictionary -- 10.3.2 Practice: Creating the Dictionary -- 10.4 Calculate the Similarity -- 10.4.1 Theoretical Part -- 10.4.2 Practice Part -- 10.5 Discussion About the Experiment -- 10.5.1 Increasing the Dictionary Scale -- 10.5.2 Similarity Score Processing -- 10.5.3 Processing the Keyframes -- 10.5.4 Validation of the Detected Loops -- 10.5.5 Relationship with Machine Learning -- 11 Dense Reconstruction -- 11.1 Brief Introduction -- 11.2 Monocular Dense Reconstruction -- 11.2.1 Stereo Vision -- 11.2.2 Epipolar Line Search and Block Matching -- 11.2.3 Gaussian Depth Filters -- 11.3 Practice: Monocular Dense Reconstruction -- 11.3.1 Discussion -- 11.3.2 Pixel Gradients -- 11.3.3 Inverse Depth Filter -- 11.3.4 Pre-Transform the Image -- 11.3.5 Parallel Computing -- 11.3.6 Other Improvements -- 11.4 Dense RGB-D Mapping -- 11.4.1 Practice: RGB-D Point Cloud Mapping -- 11.4.2 Building Meshes from Point Cloud -- 11.4.3 Octo-Mapping -- 11.4.4 Practice: Octo-mapping -- 11.5 *TSDF and RGB-D Fusion Series -- 11.6 Summary -- 12 Practice: Stereo Visual Odometry -- 12.1 Why Do We Have a Separate Engineering Chapter? -- 12.2 Framework -- 12.2.1 Data Structure -- 12.2.2 Pipeline -- 12.3 Implementation -- 12.3.1 Implement the Basic Data Structure -- 12.3.2 Implement the Frontend -- 12.3.3 Implement the Backend -- 12.4 Experiment Results -- 13 Discussions and Outlook -- 13.1 Open-Source Implementations -- 13.1.1 MonoSLAM -- 13.1.2 PTAM -- 13.1.3 ORB-SLAM Series -- 13.1.4 LSD-SLAM -- 13.1.5 SVO -- 13.1.6 RTAB-MAP -- 13.1.7 Others -- 13.2 SLAM in Future -- 13.2.1 IMU Integrated VSLAM.
13.2.2 Semantic SLAM -- Appendix A Gaussian Distribution -- A.1 Gaussian Distribution -- A.2 Transform of Gaussian Variables -- A.2.1 Linear Transform -- A.2.2 Normalized Product -- A.2.3 Joint and Conditional Distribution -- A.3 Example of Joint Distribution -- Appendix B Matrix Derivatives -- B.1 Scalar Function with Vector Variable -- B.2 Vector Function with Vector Variable -- Appendix References.
Record Nr. UNINA-9910502633303321
Gao Xiang, L.L.M.  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to visual SLAM : from theory to practice / / Xiang Gao, Tao Zhang
Introduction to visual SLAM : from theory to practice / / Xiang Gao, Tao Zhang
Autore Gao Xiang, L.L.M.
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (386 pages)
Disciplina 621.38456
Soggetto topico Wireless localization
Computer vision
Sensor networks
ISBN 981-16-4939-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- What is This Book Talking About? -- How to Use This Book? -- Source Code -- Targeted Readers -- Style -- Exercises (Self-test Questions) -- Acknowledgments -- Contents -- Part I Fundamental Knowledge -- 1 Introduction to SLAM -- 1.1 Meet ``Little Carrot'' -- 1.1.1 Monocular Camera -- 1.1.2 Stereo Cameras and RGB-D Cameras -- 1.2 Classical Visual SLAM Framework -- 1.2.1 Visual Odometry -- 1.2.2 Backend Optimization -- 1.2.3 Loop Closing -- 1.2.4 Mapping -- 1.3 Mathematical Formulation of SLAM Problems -- 1.4 Practice: Basics -- 1.4.1 Installing Linux -- 1.4.2 Hello SLAM -- 1.4.3 Use CMake -- 1.4.4 Use Libraries -- 1.4.5 Use IDE -- 2 3D Rigid Body Motion -- 2.1 Rotation Matrix -- 2.1.1 Points, Vectors, and Coordinate Systems -- 2.1.2 Euclidean Transforms Between Coordinate Systems -- 2.1.3 Transform Matrix and Homogeneous Coordinates -- 2.2 Practice: Use Eigen -- 2.3 Rotation Vectors and the Euler Angles -- 2.3.1 Rotation Vectors -- 2.3.2 Euler Angles -- 2.4 Quaternions -- 2.4.1 Quaternion Operations -- 2.4.2 Use Quaternion to Represent a Rotation -- 2.4.3 Conversion of Quaternions to Other Rotation Representations -- 2.5 Affine and Projective Transformation -- 2.6 Practice: Eigen Geometry Module -- 2.6.1 Data Structure of the Eigen Geometry Module -- 2.6.2 Coordinate Transformation Example -- 2.7 Visualization Demo -- 2.7.1 Plotting Trajectory -- 2.7.2 Displaying Camera Pose -- 3 Lie Group and Lie Algebra -- 3.1 Basics of Lie Group and Lie Algebra -- 3.1.1 Group -- 3.1.2 Introduction of the Lie Algebra -- 3.1.3 The Definition of Lie Algebra -- 3.1.4 Lie Algebra mathfrakso(3) -- 3.1.5 Lie Algebra mathfrakse(3) -- 3.2 Exponential and Logarithmic Mapping -- 3.2.1 Exponential Map of SO(3) -- 3.2.2 Exponential Map of SE(3) -- 3.3 Lie Algebra Derivation and Perturbation Model -- 3.3.1 BCH Formula and Its Approximation.
3.3.2 Derivative on SO(3) -- 3.3.3 Derivative Model -- 3.3.4 Perturbation Model -- 3.3.5 Derivative on SE(3) -- 3.4 Practice: Sophus -- 3.4.1 Basic Usage of Sophus -- 3.4.2 Example: Evaluating the Trajectory -- 3.5 Similar Transform Group and Its Lie Algebra -- 3.6 Summary -- 4 Cameras and Images -- 4.1 Pinhole Camera Models -- 4.1.1 Pinhole Camera Geometry -- 4.1.2 Distortion -- 4.1.3 Stereo Cameras -- 4.1.4 RGB-D Cameras -- 4.2 Images -- 4.3 Practice: Images in Computer Vision -- 4.3.1 Basic Usage of OpenCV -- 4.3.2 Basic OpenCV Images Operations -- 4.3.3 Image Undistortion -- 4.4 Practice: 3D Vision -- 4.4.1 Stereo Vision -- 4.4.2 RGB-D Vision -- 5 Nonlinear Optimization -- 5.1 State Estimation -- 5.1.1 From Batch State Estimation to Least-Square -- 5.1.2 Introduction to Least-Squares -- 5.1.3 Example: Batch State Estimation -- 5.2 Nonlinear Least-Square Problem -- 5.2.1 The First and Second-Order Method -- 5.2.2 The Gauss-Newton Method -- 5.2.3 The Levernberg-Marquatdt Method -- 5.2.4 Conclusion -- 5.3 Practice: Curve Fitting -- 5.3.1 Curve Fitting with Gauss-Newton -- 5.3.2 Curve Fitting with Google Ceres -- 5.3.3 Curve Fitting with g2o -- 5.4 Summary -- Part II SLAM Technologies -- 6 Visual Odometry: Part I -- 6.1 Feature Method -- 6.1.1 ORB Feature -- 6.1.2 Feature Matching -- 6.2 Practice: Feature Extraction and Matching -- 6.2.1 ORB Features in OpenCV -- 6.2.2 ORB Features from Scratch -- 6.2.3 Calculate the Camera Motion -- 6.3 2D-2D: Epipolar Geometry -- 6.3.1 Epipolar Constraints -- 6.3.2 Essential Matrix -- 6.3.3 Homography -- 6.4 Practice: Solving Camera Motion with Epipolar Constraints -- 6.4.1 Discussion -- 6.5 Triangulation -- 6.6 Practice: Triangulation -- 6.6.1 Triangulation with OpenCV -- 6.6.2 Discussion -- 6.7 3D-2D PnP -- 6.7.1 Direct Linear Transformation -- 6.7.2 P3P -- 6.7.3 Solve PnP by Minimizing the Reprojection Error.
6.8 Practice: Solving PnP -- 6.8.1 Use EPnP to Solve the Pose -- 6.8.2 Pose Estimation from Scratch -- 6.8.3 Optimization by g2o -- 6.9 3D-3D Iterative Closest Point (ICP) -- 6.9.1 Using Linear Algebra (SVD) -- 6.9.2 Using Non-linear Optimization -- 6.10 Practice: Solving ICP -- 6.10.1 Using SVD -- 6.10.2 Using Non-linear Optimization -- 6.11 Summary -- 7 Visual Odometry: Part II -- 7.1 The Motivation of the Direct Method -- 7.2 2D Optical Flow -- 7.2.1 Lucas-Kanade Optical Flow -- 7.3 Practice: LK Optical Flow -- 7.3.1 LK Flow in OpenCV -- 7.3.2 Optical Flow with Gauss-Newton Method -- 7.3.3 Summary of the Optical Flow Practice -- 7.4 Direct Method -- 7.4.1 Derivation of the Direct Method -- 7.4.2 Discussion of Direct Method -- 7.5 Practice: Direct method -- 7.5.1 Single-Layer Direct Method -- 7.5.2 Multi-layer Direct Method -- 7.5.3 Discussion -- 7.5.4 Advantages and Disadvantages of the Direct Method -- 8 Filters and Optimization Approaches: Part I -- 8.1 Introduction -- 8.1.1 State Estimation from Probabilistic Perspective -- 8.1.2 Linear Systems and the Kalman Filter -- 8.1.3 Nonlinear Systems and the EKF -- 8.1.4 Discussion About KF and EKF -- 8.2 Bundle Adjustment and Graph Optimization -- 8.2.1 The Projection Model and Cost Function -- 8.2.2 Solving Bundle Adjustment -- 8.2.3 Sparsity -- 8.2.4 Minimal Example of BA -- 8.2.5 Schur Trick -- 8.2.6 Robust Kernels -- 8.2.7 Summary -- 8.3 Practice: BA with Ceres -- 8.3.1 BAL Dataset -- 8.3.2 Solving BA in Ceres -- 8.4 Practice: BA with g2o -- 8.5 Summary -- 9 Filters and Optimization Approaches: Part II -- 9.1 Sliding Window Filter and Optimization -- 9.1.1 Controlling the Structure of BA -- 9.1.2 Sliding Window -- 9.2 Pose Graph Optimization -- 9.2.1 Definition of Pose Graph -- 9.2.2 Residuals and Jacobians -- 9.3 Practice: Pose Graph -- 9.3.1 Pose Graph Using g2o Built-in Classes.
9.3.2 Pose Graph Using Sophus -- 9.4 Summary -- 10 Loop Closure -- 10.1 Loop Closure and Detection -- 10.1.1 Why Loop Closure Is Needed -- 10.1.2 How to Close the Loops -- 10.1.3 Precision and Recall -- 10.2 Bag of Words -- 10.3 Train the Dictionary -- 10.3.1 The Structure of Dictionary -- 10.3.2 Practice: Creating the Dictionary -- 10.4 Calculate the Similarity -- 10.4.1 Theoretical Part -- 10.4.2 Practice Part -- 10.5 Discussion About the Experiment -- 10.5.1 Increasing the Dictionary Scale -- 10.5.2 Similarity Score Processing -- 10.5.3 Processing the Keyframes -- 10.5.4 Validation of the Detected Loops -- 10.5.5 Relationship with Machine Learning -- 11 Dense Reconstruction -- 11.1 Brief Introduction -- 11.2 Monocular Dense Reconstruction -- 11.2.1 Stereo Vision -- 11.2.2 Epipolar Line Search and Block Matching -- 11.2.3 Gaussian Depth Filters -- 11.3 Practice: Monocular Dense Reconstruction -- 11.3.1 Discussion -- 11.3.2 Pixel Gradients -- 11.3.3 Inverse Depth Filter -- 11.3.4 Pre-Transform the Image -- 11.3.5 Parallel Computing -- 11.3.6 Other Improvements -- 11.4 Dense RGB-D Mapping -- 11.4.1 Practice: RGB-D Point Cloud Mapping -- 11.4.2 Building Meshes from Point Cloud -- 11.4.3 Octo-Mapping -- 11.4.4 Practice: Octo-mapping -- 11.5 *TSDF and RGB-D Fusion Series -- 11.6 Summary -- 12 Practice: Stereo Visual Odometry -- 12.1 Why Do We Have a Separate Engineering Chapter? -- 12.2 Framework -- 12.2.1 Data Structure -- 12.2.2 Pipeline -- 12.3 Implementation -- 12.3.1 Implement the Basic Data Structure -- 12.3.2 Implement the Frontend -- 12.3.3 Implement the Backend -- 12.4 Experiment Results -- 13 Discussions and Outlook -- 13.1 Open-Source Implementations -- 13.1.1 MonoSLAM -- 13.1.2 PTAM -- 13.1.3 ORB-SLAM Series -- 13.1.4 LSD-SLAM -- 13.1.5 SVO -- 13.1.6 RTAB-MAP -- 13.1.7 Others -- 13.2 SLAM in Future -- 13.2.1 IMU Integrated VSLAM.
13.2.2 Semantic SLAM -- Appendix A Gaussian Distribution -- A.1 Gaussian Distribution -- A.2 Transform of Gaussian Variables -- A.2.1 Linear Transform -- A.2.2 Normalized Product -- A.2.3 Joint and Conditional Distribution -- A.3 Example of Joint Distribution -- Appendix B Matrix Derivatives -- B.1 Scalar Function with Vector Variable -- B.2 Vector Function with Vector Variable -- Appendix References.
Record Nr. UNISA-996464504303316
Gao Xiang, L.L.M.  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
MELT 2015 : proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-Less Environments : November 3th, 2015, Seattle, WA, USA / / editor(s), Ying Zhang, Bodhi Priyantha
MELT 2015 : proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-Less Environments : November 3th, 2015, Seattle, WA, USA / / editor(s), Ying Zhang, Bodhi Priyantha
Pubbl/distr/stampa New York : , : ACM, , 2015
Descrizione fisica 1 online resource (34 pages)
Disciplina 621.38456
Soggetto topico Wireless localization
Location-based services
Mobile communication systems
ISBN 1-4503-3968-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Mobile Entity Localization and Tracking in GPS-Less Environments 2015
Proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments
Record Nr. UNINA-9910376360803321
New York : , : ACM, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Mobile positioning and tracking : from conventional to cooperative techniques / / [edited by] Simone Frattasi, Francescantonio Della Rosa
Mobile positioning and tracking : from conventional to cooperative techniques / / [edited by] Simone Frattasi, Francescantonio Della Rosa
Autore Frattasi Simone S.
Edizione [Second edition.]
Pubbl/distr/stampa Chichester, UK ; ; Hoboken, NJ : , : John Wiley & Sons, , 2017
Descrizione fisica 1 PDF (416 pages)
Disciplina 621.384
Collana Wiley - IEEE
Soggetto topico Location-based services
Wireless localization
Mobile geographic information systems
Electronics in navigation
ISBN 1-119-06882-7
1-119-06885-1
1-119-06884-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- About the Authors xv -- List of Contributors xvii -- Preface xix -- Acknowledgements xxi -- List of Abbreviations xxiii -- Notations xxxi -- 1 Introduction 1 /Joaõ Figueiras, Francescantonio Della Rosa and Simone Frattasi -- 1.1 Application Areas of Positioning (Chapter 2) 5 -- 1.2 Basics of Wireless Communications for Positioning (Chapter 3) 5 -- 1.3 Fundamentals of Positioning (Chapter 4) 5 -- 1.4 Data Fusion and Filtering Techniques (Chapter 5) 6 -- 1.5 Fundamentals of Tracking (Chapter 6) 6 -- 1.6 Error Mitigation Techniques (Chapter 7) 7 -- 1.7 Positioning Systems and Technologies (Chapter 8) 7 -- 1.8 Ultrawideband Positioning and Tracking (Chapter 9) 8 -- 1.9 Indoor Positioning in WLAN (Chapter 10) 8 -- 1.10 Cooperative Multi-tag Localization in RFID Systems (Chapter 11) 9 -- 1.11 Cooperative Mobile Positioning (Chapter 12) 9 -- 2 Application Areas of Positioning 11 /Simone Frattasi -- 2.1 Introduction 11 -- 2.2 Localization Framework 11 -- 2.3 Location-based Services 13 -- 2.3.1 LBS Ecosystem 13 -- 2.3.2 Taxonomies 15 -- 2.3.3 Context Awareness 26 -- 2.3.4 Privacy 29 -- 2.4 Location-based Network Optimization 32 -- 2.4.1 Radio Network Planning 32 -- 2.4.2 Radio Resource Management 32 -- 2.5 Patent Trends 35 -- 2.6 Conclusions 39 -- 3 Basics of Wireless Communications for Positioning 43 /Gilberto Berardinelli and Nicola Marchetti -- 3.1 Introduction 43 -- 3.2 Radio Propagation 44 -- 3.2.1 Path Loss 45 -- 3.2.2 Shadowing 48 -- 3.2.3 Small-scale Fading 49 -- 3.2.4 Radio Propagation and Mobile Positioning 52 -- 3.2.5 RSS-based Positioning 54 -- 3.3 Multiple-antenna Techniques 55 -- 3.3.1 Spatial Diversity 55 -- 3.3.2 Spatial Multiplexing 56 -- 3.3.3 Gains Obtained by Exploiting the Spatial Domain 57 -- 3.3.4 MIMO and Mobile Positioning 59 -- 3.4 Duplexing Methods 59 -- 3.4.1 Simplex Systems 59 -- 3.4.2 Half-duplex 59 -- 3.4.3 Full Duplex 60 -- 3.5 Modulation and Multiple-access Techniques 61 -- 3.5.1 Modulation Techniques 61 -- 3.5.2 Multiple-access Techniques 65.
3.5.3 OFDMA and Mobile Positioning 67 -- 3.6 Radio Resource Management and Mobile Positioning 67 -- 3.6.1 Handoff, Channel Reuse and Interference Adaptation 67 -- 3.6.2 Power Control 69 -- 3.7 Synchronization 70 -- 3.7.1 Centralized Synchronization 70 -- 3.7.2 Distributed Synchronization 71 -- 3.8 Cooperative Communications 72 -- 3.8.1 Cooperative MIMO 73 -- 3.8.2 Clustering 74 -- 3.8.3 Cooperative Routing 75 -- 3.8.4 RSS-based Cooperative Positioning 75 -- 3.9 Cognitive Radio and Mobile Positioning 75 -- 3.10 Conclusions 78 -- 4 Fundamentals of Positioning 81 /João Figueiras -- 4.1 Introduction 81 -- 4.2 Classification of Positioning Infrastructures 81 -- 4.2.1 Positioning-system Topology 82 -- 4.2.2 Physical Coverage Range 83 -- 4.2.3 Integration of Positioning Solutions 84 -- 4.3 Types of Measurements and Methods for their Estimation 85 -- 4.3.1 Cell ID 85 -- 4.3.2 Signal Strength 85 -- 4.3.3 Time of Arrival 86 -- 4.3.4 Time Difference of Arrival 87 -- 4.3.5 Angle of Arrival 88 -- 4.3.6 Personal-information Identification 89 -- 4.4 Positioning Techniques 89 -- 4.4.1 Proximity Sensing 89 -- 4.4.2 Triangulation 91 -- 4.4.3 Fingerprinting 95 -- 4.4.4 Dead Reckoning 98 -- 4.4.5 Hybrid Approaches 98 -- 4.5 Error Sources in Positioning 100 -- 4.5.1 Propagation 100 -- 4.5.2 Geometry 104 -- 4.5.3 Equipment and Technology 105 -- 4.6 Metrics of Location Accuracy 106 -- 4.6.1 Circular Error Probability 106 -- 4.6.2 Dilution of Precision 106 -- 4.6.3 Cramér / Rao Lower Bound 107 -- 4.7 Conclusions 107 -- 5 Data Fusion and Filtering Techniques 109 /João Figueiras -- 5.1 Introduction 109 -- 5.2 Least-squares Methods 110 -- 5.2.1 Linear Least Squares 111 -- 5.2.2 Recursive Least Squares 112 -- 5.2.3 Weighted Nonlinear Least Squares 113 -- 5.2.4 The Absolute/Local-minimum Problem 117 -- 5.3 Bayesian Filtering 117 -- 5.3.1 The Kalman Filter 118 -- 5.3.2 The Particle Filter 124 -- 5.3.3 Grid-based Methods 126 -- 5.4 Estimating Model Parameters and Biases in Observations 126 -- 5.4.1 Precalibration 127.
5.4.2 Joint Parameter and State Estimation 127 -- 5.5 Alternative Approaches 128 -- 5.5.1 Fingerprinting 128 -- 5.5.2 Time Series Data 131 -- 5.6 Conclusions 132 -- 6 Fundamentals of Tracking 135 /João Figueiras -- 6.1 Introduction 135 -- 6.2 Impact of User Mobility on Positioning 136 -- 6.2.1 Localizing Static Devices 136 -- 6.2.2 Added Complexity in Tracking 136 -- 6.2.3 Additional Knowledge in Cooperative Environments 136 -- 6.3 Mobility Models 137 -- 6.3.1 Conventional Models 137 -- 6.3.2 Models Based on Stochastic Processes 137 -- 6.3.3 Geographical-restriction Models 144 -- 6.3.4 Group Mobility Models 146 -- 6.3.5 Social-based Models 147 -- 6.4 Tracking Moving Devices 150 -- 6.4.1 Mitigating Obstructions in the Propagation Conditions 150 -- 6.4.2 Tracking Nonmaneuvering Targets 151 -- 6.4.3 Tracking Maneuvering Targets 152 -- 6.4.4 Learning Position and Trajectory Patterns 155 -- 6.5 Conclusions 160 -- 7 Error Mitigation Techniques 163 /Ismail Guvenc -- 7.1 Introduction 163 -- 7.2 System Model 165 -- 7.2.1 Maximum-likelihood Algorithm for LOS Scenarios 166 -- 7.2.2 Cramér / Rao Lower Bounds for LOS Scenarios 167 -- 7.3 NLOS Scenarios: Fundamental Limits and Maximum-likelihood Solutions 170 -- 7.3.1 ML-based Algorithms 170 -- 7.3.2 Cramér / Rao Lower Bound 173 -- 7.4 Least-squares Techniques for NLOS Localization 175 -- 7.4.1 Weighted Least Squares 175 -- 7.4.2 Residual-weighting Algorithm 176 -- 7.5 Constraint-based Techniques for NLOS Localization 178 -- 7.5.1 Constrained LS Algorithm and Quadratic Programming 178 -- 7.5.2 Linear Programming 178 -- 7.5.3 Geometry-constrained Location Estimation 180 -- 7.5.4 Interior-point Optimization 181 -- 7.6 Robust Estimators for NLOS Localization 182 -- 7.6.1 Huber M-estimator 182 -- 7.6.2 Least Median Squares 183 -- 7.6.3 Other Robust Estimation Options 184 -- 7.7 Identify and Discard Techniques for NLOS Localization 184 -- 7.7.1 Residual Test Algorithm 184 -- 7.8 Conclusions 188 -- 8 Positioning Systems and Technologies 189 /Andreas Waadt, Guido Bruck and Peter Jung.
8.1 Introduction 189 -- 8.2 Satellite Positioning 190 -- 8.2.1 Overview 190 -- 8.2.2 Basic Principles 191 -- 8.2.3 Satellite Positioning Systems 194 -- 8.2.4 Accuracy and Reliability 195 -- 8.2.5 Drawbacks When Applied to Mobile Positioning 195 -- 8.3 Cellular Positioning 196 -- 8.3.1 Overview 196 -- 8.3.2 GSM 197 -- 8.3.3 UMTS 206 -- 8.3.4 LTE 208 -- 8.3.5 Emergency Applications in Cellular Networks 211 -- 8.3.6 Drawbacks When Applied to Mobile Positioning 213 -- 8.4 Wireless Local/Personal Area Network Positioning 213 -- 8.4.1 Solutions on Top of Wireless Local Networks 213 -- 8.4.2 Dedicated Solutions 217 -- 8.5 Ad hoc Positioning 220 -- 8.6 Hybrid Positioning 220 -- 8.6.1 Heterogeneous Positioning 220 -- 8.6.2 Cellular and WLAN 221 -- 8.6.3 Assisted GPS 221 -- 8.7 Conclusions 223 -- Acknowledgements 223 -- 9 Ultra-wideband Positioning and Tracking 225 /Davide Dardari -- 9.1 Introduction 225 -- 9.2 UWB Technology 226 -- 9.2.1 History and Definitions 226 -- 9.2.2 Theory 226 -- 9.2.3 Regulations 228 -- 9.3 The UWB Radio Channel 230 -- 9.3.1 Path Loss 231 -- 9.3.2 Multipath 231 -- 9.3.3 UWB Channel Models for Positioning 232 -- 9.4 UWB Standards 233 -- 9.4.1 IEEE 802.15.4a Standard 233 -- 9.4.2 IEEE 802.15.4f Standard 235 -- 9.4.3 Other Standards 237 -- 9.5 Time-of-arrival Measurements 237 -- 9.5.1 Two-way Ranging 237 -- 9.5.2 Time Difference of Arrival 238 -- 9.5.3 Fundamental Limits in TOA Estimation 238 -- 9.5.4 Main Issues in TOA Estimation 240 -- 9.5.5 Clock Drift 242 -- 9.6 Ranging Algoritms in Real Conditions 243 -- 9.6.1 ML TOA Estimation in the Presence of a Multipath 243 -- 9.6.2 Clock Drift Mitigation 248 -- 9.6.3 Localization and Tracking with UWB 250 -- 9.7 Passive UWB Localization 253 -- 9.7.1 UWB-RFID 253 -- 9.8 Conclusions and Perspectives 258 -- Acknowledgments 260 -- 10 Indoor Positioning in WLAN 261 /Francescantonio Della Rosa, Mauro Pelosi and Jari Nurmi -- 10.1 Introduction 261 -- 10.2 Potential and Limitations of WLAN 262 -- 10.3 Empirical Approaches 263.
10.3.1 Probe Requests and Beacon Frames 264 -- 10.3.2 Positioning Methods 265 -- 10.3.3 Evaluation Criteria for Indoor Positioning Systems Based on WLANs 272 -- 10.4 Error Sources in RSS Measurements 274 -- 10.4.1 Heterogeneous WiFi Cards 275 -- 10.4.2 Device Orientation 277 -- 10.4.3 Channel in the Presence of the User and Body Loss 278 -- 10.4.4 The Hand Grip 278 -- 10.5 Experimental Activities 279 -- 10.6 Conclusions 281 -- 11 Cooperative Multi-tag Localization in RFID Systems: Exploiting Multiplicity, Diversity and Polarization of Tags 283 /Tanveer Bhuiyan and Simone Frattasi -- 11.1 Introduction 283 -- 11.2 RFID Positioning Systems 285 -- 11.2.1 Single-tag Localization 285 -- 11.3 Cooperative Multi-tag Localization 286 -- 11.3.1 Multi-tagged Objects and Persons 286 -- 11.3.2 Localization of Mobile RFID Readers: CoopAOA 290 -- 11.3.3 Performance Evaluation 297 -- 11.3.4 Experimental Activity for Tag Localization 309 -- 11.4 Conclusions 314 -- 12 Cooperative Mobile Positioning 315 /Simone Frattasi, Joaõ Figueiras and Francescantonio Della Rosa -- 12.1 Introduction 315 -- 12.2 Cooperative Localization 316 -- 12.2.1 Robot Networks 316 -- 12.2.2 Wireless Sensor Networks 317 -- 12.2.3 Wireless Mobile Networks 321 -- 12.3 Cooperative Data Fusion and Filtering Techniques 323 -- 12.3.1 Coop-WNLLS: Cooperative Weighted Nonlinear Least Squares 323 -- 12.3.2 Coop-EKF: Cooperative Extended Kalman Filter 326 -- 12.4 COMET: A Cooperative Mobile Positioning System 328 -- 12.4.1 System Architecture 328 -- 12.4.2 Data Fusion Methods 330 -- 12.4.3 Performance Evaluation 337 -- 12.5 Experimental Activity in a Cooperative WLAN Scenario 349 -- 12.5.1 Scenario 350 -- 12.5.2 Results 350 -- 12.6 Conclusions 352 -- References 353 -- Index 373.
Record Nr. UNINA-9910270882203321
Frattasi Simone S.  
Chichester, UK ; ; Hoboken, NJ : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Mobile positioning and tracking : from conventional to cooperative techniques / / [edited by] Simone Frattasi, Francescantonio Della Rosa
Mobile positioning and tracking : from conventional to cooperative techniques / / [edited by] Simone Frattasi, Francescantonio Della Rosa
Autore Frattasi Simone S.
Edizione [Second edition.]
Pubbl/distr/stampa Chichester, UK ; ; Hoboken, NJ : , : John Wiley & Sons, , 2017
Descrizione fisica 1 PDF (416 pages)
Disciplina 621.384
Collana Wiley - IEEE
Soggetto topico Location-based services
Wireless localization
Mobile geographic information systems
Electronics in navigation
ISBN 1-119-06882-7
1-119-06885-1
1-119-06884-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- About the Authors xv -- List of Contributors xvii -- Preface xix -- Acknowledgements xxi -- List of Abbreviations xxiii -- Notations xxxi -- 1 Introduction 1 /Joaõ Figueiras, Francescantonio Della Rosa and Simone Frattasi -- 1.1 Application Areas of Positioning (Chapter 2) 5 -- 1.2 Basics of Wireless Communications for Positioning (Chapter 3) 5 -- 1.3 Fundamentals of Positioning (Chapter 4) 5 -- 1.4 Data Fusion and Filtering Techniques (Chapter 5) 6 -- 1.5 Fundamentals of Tracking (Chapter 6) 6 -- 1.6 Error Mitigation Techniques (Chapter 7) 7 -- 1.7 Positioning Systems and Technologies (Chapter 8) 7 -- 1.8 Ultrawideband Positioning and Tracking (Chapter 9) 8 -- 1.9 Indoor Positioning in WLAN (Chapter 10) 8 -- 1.10 Cooperative Multi-tag Localization in RFID Systems (Chapter 11) 9 -- 1.11 Cooperative Mobile Positioning (Chapter 12) 9 -- 2 Application Areas of Positioning 11 /Simone Frattasi -- 2.1 Introduction 11 -- 2.2 Localization Framework 11 -- 2.3 Location-based Services 13 -- 2.3.1 LBS Ecosystem 13 -- 2.3.2 Taxonomies 15 -- 2.3.3 Context Awareness 26 -- 2.3.4 Privacy 29 -- 2.4 Location-based Network Optimization 32 -- 2.4.1 Radio Network Planning 32 -- 2.4.2 Radio Resource Management 32 -- 2.5 Patent Trends 35 -- 2.6 Conclusions 39 -- 3 Basics of Wireless Communications for Positioning 43 /Gilberto Berardinelli and Nicola Marchetti -- 3.1 Introduction 43 -- 3.2 Radio Propagation 44 -- 3.2.1 Path Loss 45 -- 3.2.2 Shadowing 48 -- 3.2.3 Small-scale Fading 49 -- 3.2.4 Radio Propagation and Mobile Positioning 52 -- 3.2.5 RSS-based Positioning 54 -- 3.3 Multiple-antenna Techniques 55 -- 3.3.1 Spatial Diversity 55 -- 3.3.2 Spatial Multiplexing 56 -- 3.3.3 Gains Obtained by Exploiting the Spatial Domain 57 -- 3.3.4 MIMO and Mobile Positioning 59 -- 3.4 Duplexing Methods 59 -- 3.4.1 Simplex Systems 59 -- 3.4.2 Half-duplex 59 -- 3.4.3 Full Duplex 60 -- 3.5 Modulation and Multiple-access Techniques 61 -- 3.5.1 Modulation Techniques 61 -- 3.5.2 Multiple-access Techniques 65.
3.5.3 OFDMA and Mobile Positioning 67 -- 3.6 Radio Resource Management and Mobile Positioning 67 -- 3.6.1 Handoff, Channel Reuse and Interference Adaptation 67 -- 3.6.2 Power Control 69 -- 3.7 Synchronization 70 -- 3.7.1 Centralized Synchronization 70 -- 3.7.2 Distributed Synchronization 71 -- 3.8 Cooperative Communications 72 -- 3.8.1 Cooperative MIMO 73 -- 3.8.2 Clustering 74 -- 3.8.3 Cooperative Routing 75 -- 3.8.4 RSS-based Cooperative Positioning 75 -- 3.9 Cognitive Radio and Mobile Positioning 75 -- 3.10 Conclusions 78 -- 4 Fundamentals of Positioning 81 /João Figueiras -- 4.1 Introduction 81 -- 4.2 Classification of Positioning Infrastructures 81 -- 4.2.1 Positioning-system Topology 82 -- 4.2.2 Physical Coverage Range 83 -- 4.2.3 Integration of Positioning Solutions 84 -- 4.3 Types of Measurements and Methods for their Estimation 85 -- 4.3.1 Cell ID 85 -- 4.3.2 Signal Strength 85 -- 4.3.3 Time of Arrival 86 -- 4.3.4 Time Difference of Arrival 87 -- 4.3.5 Angle of Arrival 88 -- 4.3.6 Personal-information Identification 89 -- 4.4 Positioning Techniques 89 -- 4.4.1 Proximity Sensing 89 -- 4.4.2 Triangulation 91 -- 4.4.3 Fingerprinting 95 -- 4.4.4 Dead Reckoning 98 -- 4.4.5 Hybrid Approaches 98 -- 4.5 Error Sources in Positioning 100 -- 4.5.1 Propagation 100 -- 4.5.2 Geometry 104 -- 4.5.3 Equipment and Technology 105 -- 4.6 Metrics of Location Accuracy 106 -- 4.6.1 Circular Error Probability 106 -- 4.6.2 Dilution of Precision 106 -- 4.6.3 Cramér / Rao Lower Bound 107 -- 4.7 Conclusions 107 -- 5 Data Fusion and Filtering Techniques 109 /João Figueiras -- 5.1 Introduction 109 -- 5.2 Least-squares Methods 110 -- 5.2.1 Linear Least Squares 111 -- 5.2.2 Recursive Least Squares 112 -- 5.2.3 Weighted Nonlinear Least Squares 113 -- 5.2.4 The Absolute/Local-minimum Problem 117 -- 5.3 Bayesian Filtering 117 -- 5.3.1 The Kalman Filter 118 -- 5.3.2 The Particle Filter 124 -- 5.3.3 Grid-based Methods 126 -- 5.4 Estimating Model Parameters and Biases in Observations 126 -- 5.4.1 Precalibration 127.
5.4.2 Joint Parameter and State Estimation 127 -- 5.5 Alternative Approaches 128 -- 5.5.1 Fingerprinting 128 -- 5.5.2 Time Series Data 131 -- 5.6 Conclusions 132 -- 6 Fundamentals of Tracking 135 /João Figueiras -- 6.1 Introduction 135 -- 6.2 Impact of User Mobility on Positioning 136 -- 6.2.1 Localizing Static Devices 136 -- 6.2.2 Added Complexity in Tracking 136 -- 6.2.3 Additional Knowledge in Cooperative Environments 136 -- 6.3 Mobility Models 137 -- 6.3.1 Conventional Models 137 -- 6.3.2 Models Based on Stochastic Processes 137 -- 6.3.3 Geographical-restriction Models 144 -- 6.3.4 Group Mobility Models 146 -- 6.3.5 Social-based Models 147 -- 6.4 Tracking Moving Devices 150 -- 6.4.1 Mitigating Obstructions in the Propagation Conditions 150 -- 6.4.2 Tracking Nonmaneuvering Targets 151 -- 6.4.3 Tracking Maneuvering Targets 152 -- 6.4.4 Learning Position and Trajectory Patterns 155 -- 6.5 Conclusions 160 -- 7 Error Mitigation Techniques 163 /Ismail Guvenc -- 7.1 Introduction 163 -- 7.2 System Model 165 -- 7.2.1 Maximum-likelihood Algorithm for LOS Scenarios 166 -- 7.2.2 Cramér / Rao Lower Bounds for LOS Scenarios 167 -- 7.3 NLOS Scenarios: Fundamental Limits and Maximum-likelihood Solutions 170 -- 7.3.1 ML-based Algorithms 170 -- 7.3.2 Cramér / Rao Lower Bound 173 -- 7.4 Least-squares Techniques for NLOS Localization 175 -- 7.4.1 Weighted Least Squares 175 -- 7.4.2 Residual-weighting Algorithm 176 -- 7.5 Constraint-based Techniques for NLOS Localization 178 -- 7.5.1 Constrained LS Algorithm and Quadratic Programming 178 -- 7.5.2 Linear Programming 178 -- 7.5.3 Geometry-constrained Location Estimation 180 -- 7.5.4 Interior-point Optimization 181 -- 7.6 Robust Estimators for NLOS Localization 182 -- 7.6.1 Huber M-estimator 182 -- 7.6.2 Least Median Squares 183 -- 7.6.3 Other Robust Estimation Options 184 -- 7.7 Identify and Discard Techniques for NLOS Localization 184 -- 7.7.1 Residual Test Algorithm 184 -- 7.8 Conclusions 188 -- 8 Positioning Systems and Technologies 189 /Andreas Waadt, Guido Bruck and Peter Jung.
8.1 Introduction 189 -- 8.2 Satellite Positioning 190 -- 8.2.1 Overview 190 -- 8.2.2 Basic Principles 191 -- 8.2.3 Satellite Positioning Systems 194 -- 8.2.4 Accuracy and Reliability 195 -- 8.2.5 Drawbacks When Applied to Mobile Positioning 195 -- 8.3 Cellular Positioning 196 -- 8.3.1 Overview 196 -- 8.3.2 GSM 197 -- 8.3.3 UMTS 206 -- 8.3.4 LTE 208 -- 8.3.5 Emergency Applications in Cellular Networks 211 -- 8.3.6 Drawbacks When Applied to Mobile Positioning 213 -- 8.4 Wireless Local/Personal Area Network Positioning 213 -- 8.4.1 Solutions on Top of Wireless Local Networks 213 -- 8.4.2 Dedicated Solutions 217 -- 8.5 Ad hoc Positioning 220 -- 8.6 Hybrid Positioning 220 -- 8.6.1 Heterogeneous Positioning 220 -- 8.6.2 Cellular and WLAN 221 -- 8.6.3 Assisted GPS 221 -- 8.7 Conclusions 223 -- Acknowledgements 223 -- 9 Ultra-wideband Positioning and Tracking 225 /Davide Dardari -- 9.1 Introduction 225 -- 9.2 UWB Technology 226 -- 9.2.1 History and Definitions 226 -- 9.2.2 Theory 226 -- 9.2.3 Regulations 228 -- 9.3 The UWB Radio Channel 230 -- 9.3.1 Path Loss 231 -- 9.3.2 Multipath 231 -- 9.3.3 UWB Channel Models for Positioning 232 -- 9.4 UWB Standards 233 -- 9.4.1 IEEE 802.15.4a Standard 233 -- 9.4.2 IEEE 802.15.4f Standard 235 -- 9.4.3 Other Standards 237 -- 9.5 Time-of-arrival Measurements 237 -- 9.5.1 Two-way Ranging 237 -- 9.5.2 Time Difference of Arrival 238 -- 9.5.3 Fundamental Limits in TOA Estimation 238 -- 9.5.4 Main Issues in TOA Estimation 240 -- 9.5.5 Clock Drift 242 -- 9.6 Ranging Algoritms in Real Conditions 243 -- 9.6.1 ML TOA Estimation in the Presence of a Multipath 243 -- 9.6.2 Clock Drift Mitigation 248 -- 9.6.3 Localization and Tracking with UWB 250 -- 9.7 Passive UWB Localization 253 -- 9.7.1 UWB-RFID 253 -- 9.8 Conclusions and Perspectives 258 -- Acknowledgments 260 -- 10 Indoor Positioning in WLAN 261 /Francescantonio Della Rosa, Mauro Pelosi and Jari Nurmi -- 10.1 Introduction 261 -- 10.2 Potential and Limitations of WLAN 262 -- 10.3 Empirical Approaches 263.
10.3.1 Probe Requests and Beacon Frames 264 -- 10.3.2 Positioning Methods 265 -- 10.3.3 Evaluation Criteria for Indoor Positioning Systems Based on WLANs 272 -- 10.4 Error Sources in RSS Measurements 274 -- 10.4.1 Heterogeneous WiFi Cards 275 -- 10.4.2 Device Orientation 277 -- 10.4.3 Channel in the Presence of the User and Body Loss 278 -- 10.4.4 The Hand Grip 278 -- 10.5 Experimental Activities 279 -- 10.6 Conclusions 281 -- 11 Cooperative Multi-tag Localization in RFID Systems: Exploiting Multiplicity, Diversity and Polarization of Tags 283 /Tanveer Bhuiyan and Simone Frattasi -- 11.1 Introduction 283 -- 11.2 RFID Positioning Systems 285 -- 11.2.1 Single-tag Localization 285 -- 11.3 Cooperative Multi-tag Localization 286 -- 11.3.1 Multi-tagged Objects and Persons 286 -- 11.3.2 Localization of Mobile RFID Readers: CoopAOA 290 -- 11.3.3 Performance Evaluation 297 -- 11.3.4 Experimental Activity for Tag Localization 309 -- 11.4 Conclusions 314 -- 12 Cooperative Mobile Positioning 315 /Simone Frattasi, Joaõ Figueiras and Francescantonio Della Rosa -- 12.1 Introduction 315 -- 12.2 Cooperative Localization 316 -- 12.2.1 Robot Networks 316 -- 12.2.2 Wireless Sensor Networks 317 -- 12.2.3 Wireless Mobile Networks 321 -- 12.3 Cooperative Data Fusion and Filtering Techniques 323 -- 12.3.1 Coop-WNLLS: Cooperative Weighted Nonlinear Least Squares 323 -- 12.3.2 Coop-EKF: Cooperative Extended Kalman Filter 326 -- 12.4 COMET: A Cooperative Mobile Positioning System 328 -- 12.4.1 System Architecture 328 -- 12.4.2 Data Fusion Methods 330 -- 12.4.3 Performance Evaluation 337 -- 12.5 Experimental Activity in a Cooperative WLAN Scenario 349 -- 12.5.1 Scenario 350 -- 12.5.2 Results 350 -- 12.6 Conclusions 352 -- References 353 -- Index 373.
Record Nr. UNINA-9910677406103321
Frattasi Simone S.  
Chichester, UK ; ; Hoboken, NJ : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Positioning and Location-Based Analytics in 5G and Beyond / / edited by Stefania Bartoletti and Nicola Blefari Melazzi
Positioning and Location-Based Analytics in 5G and Beyond / / edited by Stefania Bartoletti and Nicola Blefari Melazzi
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024]
Descrizione fisica 1 online resource (291 pages)
Disciplina 621.38456
Soggetto topico Wireless localization
Location-based services
5G mobile communication systems
Wireless communication systems
ISBN 1-119-91144-3
1-119-91146-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- List of Abbreviations -- Chapter 1 Introduction and Fundamentals -- 1.1 Introduction and Motivation -- 1.2 Use Cases, Verticals, and Applications -- 1.2.1 Emergency Calls -- 1.2.2 Public Safety and Natural Disasters -- 1.2.3 ITS and Autonomous Vehicles -- 1.2.4 IIoT, Construction Sites, and Mines -- 1.2.5 Commercial and Transport Hubs -- 1.2.6 Internet‐of‐Things -- 1.2.7 Education and Gaming -- 1.3 Fundamentals of Positioning and Navigation -- 1.3.1 Position‐Dependent Measurements -- 1.3.2 Positioning Methods -- 1.3.3 AI/ML for Positioning -- 1.4 Fundamentals of Location‐Based Analytics -- 1.5 Introduction to Architectural Principles -- 1.5.1 5G Architecture and Positioning -- 1.5.2 Location‐Based Analytics Platform -- 1.6 Book Outline -- References -- Part I Positioning Enablers -- Chapter 2 Positioning Methods -- 2.1 Positioning as Parameter Estimation -- 2.1.1 The Snapshot Positioning Problem -- 2.1.2 Fisher Information and Bounds -- 2.1.3 Tracking and Location‐Data Fusion -- 2.1.3.1 Practical Aspects -- 2.2 Device‐Based Radio Positioning -- 2.2.1 Theoretical Foundations -- 2.2.1.1 Signal Model -- 2.2.1.2 Equivalent Fisher Information Matrix -- 2.2.1.3 Interpretation -- 2.2.2 Signal Processing Techniques -- 2.2.3 Example Results of 5G‐Based Positioning in IIoT Scenarios -- 2.3 Device‐Free Radio Localization -- 2.3.1 Theoretical Foundations -- 2.3.1.1 Signal Model -- 2.3.1.2 EFIM for DFL -- 2.3.1.3 Interpretation -- 2.3.2 Signal Processing Techniques -- 2.3.3 Experimental Results on 5G‐Based DFL -- 2.4 AI/ML for Positioning -- 2.4.1 Fingerprinting Approach -- 2.4.2 Soft Information‐Based Approach -- 2.4.3 AI/ML to Mitigate Practical Impairments -- References -- Chapter 3 Standardization in 5G and 5G Advanced Positioning.
3.1 Positioning Standardization Support Prior to 5G -- 3.1.1 GNSS and Real‐Time Kinematics (RTK) GNSS Positioning -- 3.1.2 WiFi/Bluetooth‐Based Positioning -- 3.1.3 Terrestrial Beacon System -- 3.1.4 Sensor Positioning -- 3.1.5 RAT‐Dependent Positioning Prior to 5G -- 3.1.5.1 Enhanced CID (eCID) -- 3.1.5.2 Observed Time‐Difference‐of‐Arrival (OTDoA) -- 3.1.5.3 Uplink Time‐Difference‐of‐Arrival (UTDoA) -- 3.1.6 Internet of Things (IoT) Positioning -- 3.1.7 Other Non‐3GPP Technologies -- 3.1.7.1 UWB -- 3.1.7.2 Fingerprinting -- 3.2 5G Positioning -- 3.2.1 5G Localization Architecture -- 3.2.2 Positioning Protocols -- 3.2.3 RAT‐Dependent NR Positioning Technologies -- 3.2.3.1 Downlink‐Based Solutions -- 3.2.3.2 Uplink‐Based Solutions -- 3.2.3.3 Downlink‐ and Uplink‐Based Solutions -- 3.2.4 Specific Positioning Signals -- 3.2.4.1 Downlink Positioning Reference Signal -- 3.2.4.2 Uplink Signal for Positioning -- 3.2.5 Positioning Measurements -- 3.3 Hybrid Positioning Technologies -- 3.3.1 Outdoor Fusion -- 3.3.2 Indoor Fusion -- 3.4 5G Advanced Positioning -- References -- Chapter 4 Enablers Toward 6G Positioning and Sensing -- 4.1 Integrated Sensing and Communication -- 4.1.1 ISAC Application: Joint Radar and Communication with Sidelink V2X -- 4.1.1.1 V2X and Its Sensing Potential -- 4.1.1.2 V2X Target Parameter Estimation and Signal Numerology -- 4.1.1.3 V2X Resource Allocation -- 4.1.2 ISAC Application: Human Activity Recognition and Person Identification -- 4.1.2.1 Beyond Positioning -- 4.1.2.2 System Aspects -- 4.1.2.3 Processing Chain (see Figure ) -- 4.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing -- 4.2.1 RIS Enabling and Enhancing Positioning -- 4.2.1.1 RIS Enabling Positioning -- 4.2.1.2 RIS Enhancing Positioning -- 4.2.1.3 Use Cases -- 4.2.2 RIS for Sensing -- 4.3 Advanced Methods -- 4.3.1 Model‐Based Methods.
4.3.2 AI‐Based Methods -- 4.3.2.1 Use Case -- References -- Chapter 5 Security, Integrity, and Privacy Aspects -- 5.1 Location Privacy -- 5.1.1 Overview on the Privacy Implication -- 5.1.2 Identification and Authentication in Cellular Networks -- 5.1.3 IMSI Catching Attack -- 5.1.4 Enhanced Privacy Protection in 5G Networks -- 5.1.5 Location Privacy Algorithms -- 5.1.6 Location Privacy Considered Model -- 5.1.7 Location Privacy Tested Approach -- 5.2 Location Security -- 5.2.1 Location Security in 4G/5G Networks -- 5.2.2 Threat Models and Bounds -- 5.2.2.1 Formal Model -- 5.2.2.2 Error Model for the Spoofing Attack -- 5.2.2.3 Threat Model Example Case Study: Range‐Based Localization Using RSSI -- 5.2.2.4 Error Bound Under Spoofing Attack -- 5.2.2.5 Case Study -- 5.3 3GPP Integrity Support -- References -- Part II Location‐based Analytics and New Services -- Chapter 6 Location and Analytics for Verticals -- 6.1 People‐Centric Analytics -- 6.1.1 Crowd Mobility Analytics -- 6.1.1.1 Introduction and Related Work -- 6.1.1.2 Example Experimental Results from Crowd Mobility Analytics: Group Inference -- 6.1.2 Flow Monitoring -- 6.1.2.1 Introduction and Related Work -- 6.1.2.2 Selected DL Approaches and Results for Trajectory Prediction -- 6.1.3 COVID-19 Contact Tracing -- 6.1.3.1 Introduction and Related Work -- 6.1.3.2 Selected Approach and Example Results from Contact Tracing -- 6.2 Localization in Road Safety Applications -- 6.2.1 Safety‐Critical Use Cases and 5G Position‐Related Requirements -- 6.2.1.1 Introduction and Related Work -- 6.2.1.2 Example Results for Safety‐Critical Use Cases -- 6.2.2 Upper Layers Architecture in ETSI ITS Standard -- 6.2.2.1 Introduction and Related Work -- 6.2.2.2 Example Results for ITS -- 6.2.3 5G Automotive Association (5GAA) Activities -- References -- Chapter 7 Location‐Aware Network Management -- 7.1 Introduction.
7.2 Location‐Aware Cellular Network Planning -- 7.2.1 What Is the Cellular Network Planning? -- 7.2.2 Why Is Localization Important in the Planning Phase? -- 7.2.3 Location‐Aware Cellular Network Planning -- 7.2.4 Future Directions -- 7.3 Location‐Aware Network Optimization -- 7.3.1 What Is the Cellular Network Optimization? -- 7.3.2 Why Is Location Information Important in Optimization? -- 7.3.3 Hybrid Clustering‐Based Optimization of 5G Mobile Networks -- 7.3.3.1 Clustering Methods and Algorithmic Approach -- 7.3.3.2 Results and Conclusions -- 7.3.4 Location‐Aware Capacity and Coverage Optimization -- 7.3.4.1 Dual‐Connectivity Optimization -- 7.3.4.2 Results and Conclusions -- 7.3.5 SINR Prediction in Presence of Correlated Shadowing in Cellular Networks -- 7.3.5.1 SINR Prediction with Kriging -- 7.3.5.2 Results and Conclusions -- 7.3.5.3 Multi‐user (MU) Scheduling Enhancement with Geolocation Information and Radio Environment Maps (REMs) -- 7.3.5.4 Results and Conclusions -- 7.3.6 Social‐Aware Load Balancing System for Crowds in Cellular Networks -- 7.3.6.1 Social‐Aware Fuzzy Logic Controller (FLC) Power Traffic Sharing (PTS) Control -- 7.3.6.2 Results and Conclusions -- 7.3.7 Future Directions -- 7.4 Location‐Aware Network Failure Management -- 7.4.1 What Is the Cellular Network Failure Management? -- 7.4.2 Why Is Localization Important in Failure Management? -- 7.4.3 Contextualized Indicators -- 7.4.3.1 Contextualized Indicators -- 7.4.3.2 Results and Conclusions -- 7.4.4 Location‐Based Deep Learning Techniques for Network Analysis -- 7.4.4.1 Synthetic mages and Deep‐Learning Classification -- 7.4.4.2 Results and Conclusions -- References -- Part III Architectural Aspects for Localization and Analytics -- Chapter 8 Location‐Based Analytics as a Service -- 8.1 Motivation for a Dedicated Platform -- 8.2 Principles.
8.2.1 Microservice Architectural Approach -- 8.2.2 Software Containerization -- 8.2.3 Mixed Kappa and Lambda Data Lake Approach -- 8.2.4 Designing an ML‐ and AI‐Aware Solution -- 8.2.5 Abstracting Computation Optimization Processes -- 8.2.6 Automating Dependency Resolution and Linking -- 8.2.7 Achieving Low Latency End‐to‐End -- 8.2.8 Decoupling Processing and API Access -- 8.2.9 Offering Dynamic Resource Allocation -- 8.2.10 Decoupling Services and Security -- 8.3 Platform System Overview -- 8.4 Platform System Blocks Description -- 8.4.1 API Blocks -- 8.4.2 Control Blocks -- 8.4.3 Core Blocks -- 8.4.4 Virtualization Management and Infrastructure Blocks -- 8.5 Functional Decomposition -- 8.5.1 Data Collection Functions -- 8.5.2 Persistence and Message Queue Functions -- 8.5.3 Positioning and Analytics Functions -- 8.5.3.1 Positioning Functions -- 8.5.3.2 Analytics Functions -- 8.5.4 Security and Privacy Functions -- 8.5.4.1 Security Functions -- 8.5.4.2 Privacy Functions -- 8.5.5 Analytics API Functions -- 8.5.6 Control Functions -- 8.5.7 Management, Orchestration, and Virtualization Functions -- 8.6 System Workflows and Data Schema Analysis -- 8.6.1 System Workflows -- 8.6.1.1 Service Activation -- 8.6.1.2 Service Consumption -- 8.6.1.3 Southbound Data Collection -- 8.6.1.4 Positioning and Analytics Service Operation -- 8.6.2 Applicable Data Schema -- 8.6.2.1 GeoJSON Data Format -- 8.6.2.2 JSON SQL Table Schema Format -- 8.6.2.3 3GPP Location Input Data -- 8.7 Platform Implementation: Available Technologies -- 8.7.1 Access Control Module -- 8.7.2 Service Discovery Module -- 8.7.3 API Gateway and Service Subscription Module -- 8.7.4 Data Operations Controller -- 8.7.5 ML Pipeline Controller -- 8.7.6 ML Model Repository -- 8.7.7 Data Collection Module -- 8.7.8 Data Persistence Module -- 8.7.9 Message Queue -- 8.7.10 Virtualization layer.
8.7.11 Management and Orchestration.
Record Nr. UNINA-9910830459503321
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Principles of wireless access and localization [[electronic resource] /] / Kaveh Pahlavan, Prashant Krishnamurthy
Principles of wireless access and localization [[electronic resource] /] / Kaveh Pahlavan, Prashant Krishnamurthy
Autore Pahlavan Kaveh <1951->
Pubbl/distr/stampa Chichester, West Sussex, U.K., : John Wiley & Sons Inc., 2013
Descrizione fisica 1 online resource (726 p.)
Disciplina 384.5
Altri autori (Persone) KrishnamurthyPrashant
Soggetto topico Wireless localization
Wireless communication systems - Access control
Soggetto genere / forma Electronic books.
ISBN 1-118-62927-2
0-470-69708-3
1-118-62928-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Principles of air-interference design -- pt. II. Principles of network infrastructure design -- pt. III. Wireless local access -- pt. IV. Wide area wireless access -- pt. V. Wireless localization.
Record Nr. UNINA-9910452595503321
Pahlavan Kaveh <1951->  
Chichester, West Sussex, U.K., : John Wiley & Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of wireless access and localization [[electronic resource] /] / Kaveh Pahlavan, Prashant Krishnamurthy
Principles of wireless access and localization [[electronic resource] /] / Kaveh Pahlavan, Prashant Krishnamurthy
Autore Pahlavan Kaveh <1951->
Pubbl/distr/stampa Chichester, West Sussex, U.K., : John Wiley & Sons Inc., 2013
Descrizione fisica xviii, 706 p. : ill. (some col.)
Altri autori (Persone) KrishnamurthyPrashant
Soggetto topico Wireless communication systems - Access control
Wireless localization
ISBN 1-118-62928-0
0-470-69708-3
1-118-62927-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Principles of air-interference design -- pt. II. Principles of network infrastructure design -- pt. III. Wireless local access -- pt. IV. Wide area wireless access -- pt. V. Wireless localization.
Record Nr. UNINA-9910796100403321
Pahlavan Kaveh <1951->  
Chichester, West Sussex, U.K., : John Wiley & Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui