**Studentlitteratur, 2010 and 2012**

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.

All algorithms and examples in the book are available in the Matlab Toolbox Signals and Systems Lab. There is a separate excercise compendium with more than 100 worked out examples.

*New edition 2012*. The new edition is basically a corrected
version of the 2010 edition. The 2010 errata list includes the most
important changes. However, there are some new formatting and
explanation that distroyed the original page numbering, thus it has to
be seen as a new edition.
*Fredrik Gustafsson* is professor in sensor informatics at Linköping
University. He has during the past 15 years managed a wide range of
sensor fusion projects, both theoretical and applied with close
industrial cooperation. During this time, he has supervised 15 PhD
students and 150 MSc students. His group at Linköping University
consists of more than 15 people engaged in sensor fusion research.

**Contact the author:**

Fredrik Gustafsson