Sensor fusion deals with merging information from two or more sensors, where the area of statistical signal processing provides a powerful toolbox to attack both theoretical and practical problems.
The objective of this book is to explain state of the art theory and algorithms in statistical sensor fusion, covering estimation, detection and nonlinear filtering theory with applications to localization, navigation 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 provided 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.
