Cyclone tracking using a single orbiting satellite in a continuous manner is impractical as it has limited spatial and temporal coverage. One solution is to use multiple orbiting satellites for cyclone tracking. However, data from some orbiting satellites do not provide features as useful as other satellites in identifying cyclones. Moreover, satellite data containing strong cyclone discriminating features may be affected by coarse temporal resolution and object occlusion. In this paper, we propose a knowledge transfer methodology based on a Kalman filter for cyclone tracking using multiple satellite data sources containing a mixture of strong and weak features. This approach minimizes the negative effect of coarse temporal resolution and occlusion if only the satellite data containing strong cyclone discriminating features were used. Experimental results are presented to demonstrate the feasibility and usefulness of our knowledge transfer approach for cyclone tracking.