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Trajectory determination and analysis in sports by satellite and inertial navigation / Adrian Wägli (2009)

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Titre :	Trajectory determination and analysis in sports by satellite and inertial navigation
Type de document :	Thèse/HDR
Auteurs :	Adrian Wägli, Auteur ; Jan Skaloud, Directeur de thèse
Editeur :	Zurich : Schweizerischen Geodatischen Kommission / Commission Géodésique Suisse
Année de publication :	2009
Collection :	Geodätisch-Geophysikalische Arbeiten in der Schweiz, ISSN 0257-1722 num. 77
Importance :	173 p.
Format :	21 x 30 cm
ISBN/ISSN/EAN :	978-3-908440-20-5
Note générale :	Bibliographie Doctoral thesis
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] filtrage du bruit [Termes IGN] GPS-INS [Termes IGN] modèle d'erreur [Termes IGN] navigation inertielle [Termes IGN] orientation [Termes IGN] positionnement par GNSS [Termes IGN] positionnement par GPS [Termes IGN] précision décimétrique [Termes IGN] sport [Termes IGN] test de performance [Termes IGN] trajectographie par GPS [Termes IGN] trajet (mobilité)
Index. décimale :	30.83 Applications océanographiques de géodésie spatiale
Résumé :	(Auteur) [Préface] The abundance and availability of small positioning devices offers new opportunities (and challenges) for the art and science of Kinematic Geodesy. Certainly, as the inventors of inertial navigation never dreamed of a full Inertial Measurement Units (IMUs) occupying space of few cubic millimeters, the designers of the Global Positioning System (GPS) never thought of placing miniature receivers on human beings. Yet, it is the variety of civil application that improves the measurement accuracy of the originally military technology by an order (or several orders) of magnitude. This can be achieved either by exploiting secondary signals or by proposing innovative algorithms. The research of Adrian Wagli belongs to the latter category as it presents (with an excellent rigor) innovative algorithms and data processing approaches which turn signals from small GPS receivers and miniature but very imprecise Micro-electromechanical (MEMS)-IMU into a convincing measurement instrument capable of tracking the skier's 2-G turn with 0.01% accuracy. The amalgam of high precision and small instrumentation then allows tracing movement of athletes not once in a while, but continuously at 100 times per second. Thus, through the practically continuous measurements of 3D position, velocity and orientation, the sportsmen's performance parameters can be deduced. Using it in sports like alpine skiing is very challenging task due to the encountered dynamic and the mountain surroundings that block the reception of satellite signals. Therefore, if the technology finds its place in such relatively hostile conditions, it can be" surely used for other purposes in more benign environment. At the same time it represents a very motivating factor for the research undertaken at the country to which such sport belongs. In his work, Adrian Wagli demonstrates for the first time that redundant configuration of low-cost MEMS-IMUs allows determining orientation better than 1 degree RMS and that the autonomous positioning of decimeter accuracy is feasible with these sensors up to 30-second long outages of GPS signals even in high dynamic. Although the thesis is application-driven, i.e. the work results in. several algorithms and software modules applicable to real scenarios; it contains, at the same time, a I number of novel concepts applicable to other domains of navigation and kinematic positioning. The nicely presented combination of theory and practice will therefore satisfy a wide spectrum of readers.
Note de contenu :	1 Introduction 1.1 Context 1.2 Particularities Related to Sport Applications 1.3 Objectives 1.4 Methodology 2 From Sports to Navigation 2.1 Criteria of Sport Applications 2.1.1 Accuracy Requirements 2.2 Methods for Trajectory Determination 2.2.1 Imagery 2.2.2 Satellite and Inertial Navigation 2.2.3 Alternative Techniques Based on Position Fixing 2.2.4 Complementary Methods to Trajectory Determination 2.2.5 Summary 2.3 Instrumentation for Satellite and Inertial Navigation 2.3.1 Overview on GNSS and Processing Methods 2.3.2 Inertial Measurement Units 2.3.3 Other Aspects Related to System Architecture 3 Measurements, Models and Estimation Methods 3.1 Inertial Measurement Model 3.1.1 Generalized Error Model for Inertial Observations 3.1.2 Simplified Error Model for Inertial Observations 3.2 Magnetic Measurements 3.3 GPS Observations 3.3.1 Code Measurements 3.3.2 Carrier-Phase Measurements 3.3.3 Carrier-Phase Smoothing 3.3.4 Doppler Measurements 3.3.5 Differential GPS 3.4 GPS/INS Sensor Fusion 3.4.1 Integration Constraints 3.4.2 Integration Strategy Trade-offs 3.4.3 Kalman Filtering 3.4.4 Optimal Smoothing 3.5 Implementation of GPS Processing 3.5.1 Definition of the State Vector 3.5.2 Initialization 3.5.3 State Propagation 3.5.4 Measurement Updates 3.6 Implementation of GPS/INS Integration 3.6.1 Definition of the State Vector 3.6.2 Initialization 3.6.3 Strapdown Inertial Navigation 3.6.4 Measurement Updates 4 GPS/MEMS-IMU System Performance 4.1 Experimental Setup 4.2 GPS/MEMS-IMU Performance 4.2.1 Satellite Navigation 4.2.2 GPS/MEMS-IMU Integration 4.2.3 GPS/MEMS-IMU Integration during Reduced Satellite Reception 4.2.4 Benefits of RTS Smoothing 4.3 Benefits of UKF 4.3.1 Navigation Performance 4.3.2 Implementation Aspects 4.4 Magnetic Sensors 4.5 Orientation Initialization 4.5.1 Evaluation based on Simulations 4.5.2 Experimental Evaluation 5 MEMS-IMU Error Modeling 5.1 Static Evaluation by Allan Variance 5.2 Static Estimation of the Noise Parameters 5.3 Dynamic Error Model Investigation 5.3.1 Estimation of the Relative Alignment of the MEMS-IMU 5.3.2 Estimation of the Reference Values for the Inertial Sensor Errors 5.3.3 Error Model Analysis 5.3.4 Relevance to Kalmari Filtering 5.4 Investigation of more Complex Error Models 6 Performance Improvement through Redundant IMUs 6.1 INS Redundancy Approaches in Inertial Navigation 6.2 Geometrical Arrangement of Redundant IMUs 6.3 Noise Reduction and Direct Noise Estimation 6.3.1 Noise Reduction 6.3.2 Direct Noise Estimation 6.4 Fault Detection and Isolation 6.5 System and Observation Model for the Redundant IMU Integration 6.5.1 Synthetic IMU Integration 6.5.2 Extended IMU Mechanization 6.5.3 Geometrically-Constrained Mechanization 6.6 Navigation Performance Improvement 6.6.1 Algorithm Selection 6.6.2 Assessment Based on Experiments 6.6.3 Assessment Based on Emulation 6.6.4 Notes on the Observability 6.6.5 Orientation Initialization and Inertial Error Estimation 7 From Navigation to Performance Assessment in Sport 7.1 Trajectory Modeling Approaches 7.1.1 Cubic Splines Smoothing 7.1.2 Additional Kalman Filtering 7.1.3 Limitations of Trajectory Modeling . 7.2 Trajectory Matching 7.2.1 Problem Definition 7.2.2 Extension of Cubic Spline Smoothing 7.2.3 Eigenvector Approach for Feature-Based Correspondence 7.2.4 Position Accuracy Improvement through Trajectory Matching 7.2.5 Risk Related to Trajectory Matching 7.3 Trajectory Comparison 7.3.1 Spatial Trajectory Comparison Approach 7.3.2 Methodology for Trajectory Comparison 7.3.3 Alternative Methods for Trajectory Comparison 7.3.4 Visualization Aspects 7.4 Position-Based Chronornetry 7.5 Orientation Related Assessment - Skiing 7.6 Orientation Related Assessment - Motorcycling 7.6.1 Reference Frame Aspects 7.6.2 Computation of the Lateral Slipping of Tires 7.6.3 Evaluation of the Tire Characteristics 7.6.4 Other Perspectives 8 Conclusions and Perspectives 8.1 Conclusions 8.2 Perspectives
Numéro de notice :	15514
Affiliation des auteurs :	non IGN
Autre URL associée :	URL EPFL
Thématique :	POSITIONNEMENT
Nature :	Thèse étrangère
DOI :	10.5075/epfl-thesis-4288
En ligne :	https://www.sgc.ethz.ch/sgc-volumes/sgk-77.pdf
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=62747

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