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.