Open AccessTraining samples selection method for space‐time adaptive processing based on clutter covariance matrix reconstruction
Author(s) -
Li Zhihui,
Zhang Yongshun,
Liu Hanwei,
Guo Yiduo
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.1549
Subject(s) - clutter , covariance matrix , selection (genetic algorithm) , computer science , space time adaptive processing , artificial intelligence , pattern recognition (psychology) , matrix (chemical analysis) , algorithm , training (meteorology) , machine learning , radar , physics , telecommunications , radar imaging , materials science , composite material , pulse doppler radar , meteorology
A novel training samples selection method is proposed to improve the performance of space‐time adaptive processing in non‐homogeneous environments, where training samples are contaminated by interference target signals (outliers). First, the outlier component is removed from the sample covariance matrix based on clutter covariance matrix reconstruction, which utilises the clutter Capon spectrum integrated over a sector separated from the location of outlier. Secondly, the integral is calculated approximately by discrete sum method. Finally, the reconstructed clutter covariance matrix is combined with the generalised inner products algorithm to form the proposed statistics and the contaminated training samples are eliminated. Simulation results validate the effectiveness of the proposed method.