Analysis of space-time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

S. Kang, J. Ryu, J. Lee, J. Jeong

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space-time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the K-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors' simulation results demonstrate that the K-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

Original languageEnglish
Pages (from-to)113-120
Number of pages8
JournalIET Signal Processing
Volume5
Issue number2
DOIs
Publication statusPublished - 2011 Apr

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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