Point-cloud clustering and tracking algorithm for radar interferometry.

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point clouds.

Measuring small-scale plasma irregularities in the high-latitude E- and F-regions simultaneously.

The ionosphere, Earth's space environment, exhibits widespread turbulent structuring, or plasma irregularities, visualized by the auroral displays seen in Earth's polar regions. Such plasma irregularities have been studied for decades, but plasma turbulence remains an elusive phenomenon. We combine scale-dependent measurements from a ground-based radar with satellite observations to characterize small-scale irregularities simultaneously in the bottomside and topside ionosphere and perform a statistical analysis on an aggregate from both instruments over time.