Real-Time Detection and Filtering of Chaff Clutter from Single-Polarization Doppler Radar Data

Abstract

Abstract In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.