Speeding Up a Large-Scale Filter

Abstract

Wolfson et al. introduced a storm tracking algorithm, called the Growth and Decay Storm Tracker, in which the large-scale features were extracted from radar data fields by using an elliptical filter. The elliptical filter as introduced was computationally too expensive to be performed in real time. In this paper, it is shown that although the elliptical filter is nonlinear, it can be decomposed into two parts, one of which is, under some simplifying assumptions, linear and shift-invariant. The linear component can be accelerated using fast algorithms available to compute the digital Fourier transform (DFT). Furthermore, it is shown that the nonlinear part can be written as an update equation, thus reducing the amount of computer memory required. With these improvements to the basic large-scale filtering technique, this paper reports that the large-scale filtering can be done 1?2 orders of magnitude faster. The improvement makes it possible to use the large-scale filtering technique in situations where the computational time and memory requirements have been prohibitive.