
- 553 pages
- English
- PDF
- Available on iOS & Android
Statistical Sensor Fusion
About this book
Sensor fusion deals with merging information from two or more sensors, where the area of statistical signal pro-cessing provides a powerful toolĀbox to attack both theoretical and practical problems. The objective of this book is to explain state of the art theory and algoĀrithms in statistical sensor fusion, covering estimation, detection and nonĀlinear filtering theory with applications to localization, naviĀgation and tracking problems. The book starts with a review of the theory on linear and nonlinear estimation, with a focus on sensor network applications. Then, general nonlinear filter theory is surveyed with a particular attention to different variants of the Kalman filter and the particle filter. Complexity and implementation issues are discussed in detail. Simultaneous localization and mapping (SLAM) is used as a challenging application area of high-dimensional nonlinear filtering problems.The book spans the whole range from mathematical foundations proĀvided in extensive appendices, to real-world problems covered in a part surveying standard sensors, motion models and applications in this field.All models and algorithms are available as object-oriented Matlab code with an extensive data file library, and the examples, which are richly used to illustrate the theory, are supplemented by fully reproducible Matlab code.
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