Facing the expansion of geolocation needs, illustrated by the GALILEO European project, the growth of Location Based Services (LBS) and the need to identify the location of emergency mobile phone calls in Europe (standard E112), the research on localization techniques is booming. This thesis focuses on indoor pedestrian navigation and investigates a localization solution based on micro-electromechanical systems (MEMS) and ultra-wideband waves (UWB). MEMS based localization estimates the current location from a previously determined position using on-board low-cost inertial embedded sensors. Unfortunately, the performances of these autonomous systems are affected by large errors (typical of these sensors). In fact standalone solutions drift rapidly with time. Impulse-Radio UWB (IR-UWB) Times Of Arrival (TOA) are often used for localization purposes. This network based technology uses sensor networks, mainly attached to the infrastructure of the building to estimate the location of the transmitter with decimetre accuracy in ideal scenarii. However the indoor environment is hostile for radio propagation. Full of artificial obstacles, electromagnetic waves are disturbed and radiolocation performances are reduced. Construction materials also affect the magnetic field used to estimate the pedestrian's walking direction. In this context, the hybridization of these two complementary and uncorrelated technologies is promising. The study of the movement pattern of a pedestrian walking indoors induces two main outcomes on localization techniques. Firstly, random pedestrian movements complicate MEMS signal processing. Secondly, when the transmitter is worn by the user, for example around the neck, IR-UWB that propagates through the human body can hardly contribute to the localization. Optimal data fusion filters that hybridize a large set of observations : Angles Of Arrival (AOA), Time Differences Of Arrival (TDOA), accelerations, angular velocities and magnetic field measurements are presented. The coupling of UWB and MEMS data relies on an Extended Kalman Filter (EKF) complemented with specific procedures. Loose integration and tight integration are considered. Outlier detection processes within the radio data enrich the EKF. The most remarkable process is based on the RANSAC paradigm and employs the physical constraints of the pedestrian's walk described by biomechanics. In some cases, it enables the processing of reflected radio signals. A user equipped with a MEMS module and an UWB transceiver walked in the premises of the EPFL, following nine independent paths, for a total length of 380 m. The benefit of the MEMS/UWB hybridization filters are evaluated based on this experiment. The tight integration outperforms the loose coupling and enables indoor pedestrian localization with a one metre accuracy.