9.4
A multi-radar data fusion method for small scale features
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While radars report positions of range resolution volumes (RRVs) based on the location of the center of the radar beam, the RRVs actually possess a complex geometry. This research provides a method for fusing multiple RRVs by using spherical trigonometry to identify horizontal overlapping regions of individual RRVs. The sampling area of the RRV increases with range from the radar, creating a larger uncertainty in the position of the objects sampled (especially in cross-beam directions). In the presented fusion technique, the beam power density from an individual radar is assumed to follow a Gaussian distribution across the RRV. The overlapping region encompasses a portion of each RRV's Gaussian curve which allows weights to be calculated that can be applied to the respective RRV data value during fusion.
Identifying the overlapping region of multiple RRVs decreases the overall target positional uncertainty (especially when the individual radars possess disparate viewing angles), and identifies weights to be used in fusing the meteorological data. Simulated results indicate an average decrease in horizontal positional uncertainty around 550 to 600 meters using the assumption of one degree beam resolution in both horizontal and vertical directions. The vertical uncertainty of the fused RRV was determined by finding the vertical overlapping region of the RRV's vertical boundaries and was shown to decrease by 250 to 350 meters.
In addition to decreasing target location uncertainties, such an approach has potential for meteorological applications by providing the degree and parameters of the spatial overlap which enables one to identify both similarities and differences between fields observed with multiple radars. Such an approach could be used as an alternative first step in producing radar mosaics wherein data from multiple-radars are first combined and then spatially analyzed to a grid. Further, information regarding differences and the orientation of RRVs could be used to help recover (deconvolve) the underlying field. Finally, this technique could be used to track small scale atmospheric phenomena such as microbursts, tornadoes, etc.