A Support-Vector-Machine-Based Approach to RF Sensor Spectral Signature Classifications

In this paper, a support vector machine (SVM) classifier was designed to identify tornado vortices based on their characteristics that were determined from the Doppler spectra and eigenvalues that were calculated from the data that were collected in the vicinity of these vortices. To collect these data, weather surveillance radar (WSR-88D) was employed, which is locally operated by the National Severe Storms Laboratory (NSSL) on the north campus of the University of Oklahoma, Norman. This particular radar, which is devoted to experimental research rather than operational weather forecasting, has the unique capability of recording massive volumes of in-phase (I) and quadrature (Q) data over many hours. As such, the received radar echo power and Doppler shift information can be determined from these I and Q complex-valued data, which provides a rich environment for evaluating our new postprocessing algorithms. At the current time, most radar systems, including North America's national network of weather radar systems, process these I and Q data in real time to produce environmental measurements known as ldquospectral moments,rdquo which are the first three statistical moments of the data. These three moments (the reflectivity, the radial velocity, and the spectrum width) are then provided to scientists or forecasters, and the abundance of I and Q data is not preserved to save computer resources. One of the aims of this paper is to show how additional information about the atmosphere can be garnered from the I and Q data. To assist this mission, an SVM-based classifier evades the pitfalls of the traditional statistical learning algorithms, such as neural networks, by setting up a convex optimization problem with a single global minimum. In addition, through the use of kernels and a nonlinear mapping to higher dimensional spaces, the SVM classifier is able to effectively handle nonlinear classification problems. The idea behind this transformation is to facilitate the separability of classes by taking the input vectors to a higher dimensional space. The SVM classifier has the added advantage of reducing overfitting by constructing a maximum margin to separate hyperplanes in a higher dimensional feature space to ensure a small generalization error bound. Finally, our practical results are in positive agreement with our theoretical predictions.