Spectral signature calculations and target tracking for remote sensing

Enhanced tornado detection and tracking can prevent loss of life and property damage. The research weather surveillance radar (WSR)-88D locally operated by the National Severe Storms Laboratory (NSSL) in Norman, OK, has the unique capability of collecting massive volumes of time-series data over many hours, which provides a rich environment for evaluating our new postprocessing algorithms. With the advent of more memory and computing power, new state-of-the-art algorithms can be explored. In this paper, an approach of identifying tornado vortices in Doppler spectra is proposed and investigated through the use of neural networks. Once the coordinate of the tornado has been established, the research question becomes the following: Can we apply target tracking algorithms to a volume of radar data to make estimations about where the tornado is going? In recent years, particle filters have attracted great attention in several research communities. These filters are used in problems where time-varying signals must be processed in real time, and the objective is to estimate various unknowns of the signals and to detect events described by the signals. The standard solutions of such problems in many applications are based on the Kalman or extended Kalman filters. In situations when the models that describe the behavior of the system are highly nonlinear and/or the noise that distorts the signals is non-Gaussian, the Kalman-filter-based algorithms provide solutions that may be far from optimal. Here, the path of the tornado follows a path that may be described by a set of nonlinear equations. To estimate the path, the particle filter will provide the better estimates. In addition to the single WSR-88D sensor designs, data fusion and tracing designs are also given for a four-node remote sensor network in central Oklahoma. By incorporating the data from each of the sensors, improvements in tracking are illustrated. The particle-filtering algorithms are especially effective in a networked system of sensors when they are in a data-fusion setting.