State estimation plays a key role in the knowledge of characteristic variables of a system. By combining information from hardware sensor measurements and mathematical models, it improves the knowledge of the relevant variables used for advanced control or monitoring strategies. Moreover, a lot of state estimation techniques exist and try to improve accuracy despite uncertainties. In this context, this work provides an overview of useful state estimation techniques and gives extensions with robustness to uncertainties as objective. Two application fields are investigated with their own objectives in terms of safety and accuracy : safe vehicle positioning and bioprocesses. This book is divided into three parts. The first part introduces state estimation and describes popular techniques as Kalman filter and moving-horizon filter. Recent methods are also introduced like particle filtering or interval estimation. The next two parts are dedicated to the applications and the developed extensions. This book has a tutorial objective and can be dedicated to engineers in Automatic Control or to all who look for an advanced introduction to the state space exploration.