Sensor fusion deals with merging information from two or more sensors, where statistical signal processing provides a powerful tool­box for attacking theoretical and practical problems. This book explains state of the art theory and algorithms in statistical sensor fusion. It covers estimation, detection and non­linear filtering theory with applications to localization, navigation and tracking problems. It starts with a review of the theory on linear and non-linear estimation, with a focus on sensor network applications. Then, general non-linear filter theory is surveyed with 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) provides challenging applications for high-dimensional non-linear filtering problems. The whole range -- from mathematical foundations provided in extensive appendices, to real-world problems -- is covered, including 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 examples, which are richly used to illustrate the theory, are supplemented by fully reproducible Matlab code. Second edition