This chapter presents several signal processing strategies to combine together, in a seamless estimation process, position-related measurements coming from different technologies and/or systems (e.g., TOA and TDOA measurements in terrestrial networks, TOA and RSS measurements, or even satellite and terrestrial systems, or satellite and inertial navigation systems). This approach, generally indicated as “hybridization”, promises to provide better accuracy with respect to its stand-alone counterparts, or better availability thanks to the diversity of the employed technologies. For example, hybridization between satellite and inertial systems is expected to compensate for the respective fragilities of the two systems, namely the relatively high error variance of the former and the drift of the latter. The mathematical framework where hybridization is developed is Bayesian filtering: the generic structure is reviewed and the well-known Kalman filter and its variants are inserted in the framework, with examples of applications to positioning problems. Then the particle filter approach is explained, with its most used variants. Examples of hybrid localization algorithms are then shown, starting from a hybrid terrestrial architecture, then passing to the architectures that blend GNSS and inertial measurements, using either the Kalman filter approach or the direct position estimation approach. Finally, an example of hybrid localization based on GNSS and peer-to-peer terrestrial signaling is presented.