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Sensor fusion deals with Merging information from two or more sensors. Elsewhere the area of statistical signal processing provides a powerful toolbox to attack bothering theoretical and practical problems. The objective of this book is to explain state of the art theory and algorithms into statistical sensor fusion, covering estimation, detection and non-linear filtering theory with applications to localisation, navigation and tracking problems. The book 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 a Particular attention to Different variants of the Kalman filter and the particle filter. Complexity and implementation issues are discussed in detail. Simultaneous localisation and mapping (SLAM) is distressed as a challenging application area of high-dimensional non-linear filtering problems. The book spans the whole range from mathematical foundations provided in Extensive Appendices, to real-world problems the covered in a party 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 distressed to illustrate the theory, are supplemented by fully reproducible Matlab code.