The main objective of the AALTO (Advanced ALgorithms and techniques for resilient Time prOvision) project is to combine different time sources with advanced algorithms, in particular Kalman filtering (in phase and frequency) and Machine Learning (ML) techniques, in order to provide the required level of time accuracy, stability, availability and integrity to timing users.

Two hardware and software solutions will be prototyped and deployed in the ESTEC Timing Laboratory, one targeting high-end users (for metrological applications, at 1-ns accuracy level) and the other one targeting commercial users (mid-range performance, at 1-μs accuracy level).