Performance comparison between statistical-based and direct data domain STAPs

Santana Burintramart, Tapan K. Sarkar, Yu Zhang, Michael C. Wicks

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

In the situation that a radar platform is moving very fast, the number of training data used in space-time adaptive processing (STAP) is a major concern. Less number of training data is preferred in this situation. In this paper, four versions of statistical-based and direct data domain STAPs are discussed and compared their performance when the number of training data is varied. The four statistical-based methods are the full-rank statistical method, the relative importance of the eigenbeam (RIE) method, the principle component generalized sidelobe canceller (GSC) method, and the cross-spectral GSC method. We will compare the performance of these four methods with that of the direct data domain least squares (D3LS) approach, which utilizes only one snapshot of data in its processing. The channel mismatch will be also introduced to all methods to evaluate their performance. It is found that to make the statistical-based methods work; we need to know the rank of the interference covariance matrix. And the D3LS performs better when the number of training data available for the statistical-based methods is less than the rank of the interference covariance matrix.

Original languageEnglish (US)
Pages (from-to)737-755
Number of pages19
JournalDigital Signal Processing: A Review Journal
Volume17
Issue number4
DOIs
StatePublished - Jul 2007

Keywords

  • Direct data domain least squares method
  • Space-time adaptive processing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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