Performance evaluation of parametric Rao and GLRT detectors with KASSPER and Bistatic Data

Pu Wang, Kwang June Sohn, Hongbin Li, Braham Himed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The parametric Rao and GLRT detectors, recently developed by exploiting a multichannel autoregressive (AR) model for the spatially and temporally colored disturbance, were shown to perform well with limited or even no range training data for the airborne radar configuration. In previous computer simulation studies of these parametric detectors, the disturbance was generated as a multichannel AR process. However, the disturbance signal in an airborne radar environment do not necessarily follow an exact multichannel AR model. In this paper, we evaluate the detection performance of the parametric Rao and GLRT detectors using more realistic datasets: the KASSPER 2002 dataset that includes many real-world effects such as heterogeneous terrains, antenna errors and leakage, and dense ground targets/discretes, etc., and the Bistatic dataset which contains range-dependent clutter due to bistatic geometry. Experimental results on both datasets show that the parametric detectors can provide good detection performance with limited or no range training in more realistic radar environments.

Original languageEnglish
Title of host publication2008 IEEE Radar Conference, RADAR 2008
DOIs
StatePublished - 2008
Event2008 IEEE Radar Conference, RADAR 2008 - Rome, Italy
Duration: 26 May 200830 May 2008

Publication series

Name2008 IEEE Radar Conference, RADAR 2008

Conference

Conference2008 IEEE Radar Conference, RADAR 2008
Country/TerritoryItaly
CityRome
Period26/05/0830/05/08

Keywords

  • KASSPER dataset
  • Multichannel signal detection
  • Space-time adaptive processing (STAP)

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