Presents an efficient method of multi-sensor estimation for the navigation of vehicles. A decentralised architecture is used for the fusion of information obtained from several asynchronous measurements. The issue of the synchronisation of the information, which is critical in the proposed method, is addressed. The information form of the Kalman filter (information filter) is used as the main algorithm for position estimation. The method is implemented as part of the navigation system for an autonomous land vehicle. The navigation system includes two independent loops which communicate through an assimilation unit. One loop incorporates inertial and GPS information and the other uses millimetre wave radar and encoder measurements to obtain local estimates. The information obtained from each measurement is then broadcast to the other loops after being synchronised. This information is used in an assimilation stage to achieve more accurate estimates. The assimilation takes place periodically, where the assimilation frequency can be selected. The performance of the navigation method is examined by comparing the resulting position estimates to those of independent navigation loops.