Probabilistic modeling for positioning applications using inertial sensors
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In this thesis, the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes) using Accelerometers and Gyroscopes is considered.Abstract:
In this thesis, we consider the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes). Inertial sensors provide information about the chang ...read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Indoor Positioning Using Ultrawideband and Inertial Measurements
TL;DR: This paper presents an approach to combine measurements from inertial sensors (accelerometers and gyroscopes) with time-of-arrival measurements from an ultrawideband (UWB) system for indoor positioning using a tightly coupled sensor fusion approach.
Journal ArticleDOI
Magnetometer Calibration Using Inertial Sensors
Manon Kok,Thomas B. Schön +1 more
TL;DR: A practical algorithm for calibrating a magnetometer for the presence of magnetic disturbances and for magnetometer sensor errors is presented and is shown to give good results using data from two different commercially available sensor units.
Identification of Time Varying Systems and Application of System Identification to Signal Processing
TL;DR: It has been observed that the quality of synthesized speech can be improved, if a more detailed model than an impulse train is used for the pitch pulses, and it is here shown how the method presented can be used to estimate the system parameters of the speech production and the parameters ofThe glottal pulse simultaneously.
A Study of Adaptive Control of Missiles
TL;DR: In this paper, the authors assess the use of adaptive control schemes to the guidance and control problem of tactical missiles, which is basically characterized by the fact that the unerlying equations are well known and apriori information of parameters can be obtained.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
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A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.