Object tracking extensions for accurate recovery of rainfall maps using microwave sensor network

Recently, diverse methods have been proposed for faithful reconstruction of instantaneous rainfall maps by using received signal level (RSL) measurements from commercial microwave network (CMN), especially in dense networks. The main lacking of these methods is that the temporal properties of the rain field had not been considered, hence their accuracy might be limited. This paper presents a novel method for accurate spatio-temporal reconstruction of rainfall maps, derived from CMN, by using an extension to object tracking algorithms. An efficient coherency algorithm is used, which relates between sequential instantaneous rainfall maps. Then by using Kalman filter, the observed rain maps are predicted and corrected. When comparing the estimates to actual rain measurements, the performance improvement of the rainfall mapping is manifested, even when dealing with a rather sparse network, and low temporal resolution of the measurements. The method proposed here is not restricted to the application of accurate rainfall mapping.