Main-lobe Interference Suppression Algorithm Based on TF-RobustICA

Suppression of main-lobe interference is a challenging problem for radar because the interference and target signal are highly coupled both in the time and frequency domains. In this paper, we propose a novel main-lobe interference suppression algorithm based on robust independent component analysis and time-frequency transformation, named as TF-RobustICA. The proposed TF-RobustICA model transforms the echo into the time-frequency domain to effectively capture the time-frequency statistics of the interference and target signal. Afterwards, the proposed TF-RobustICA separates the interference and target components based on the criterion of maximizing nongaussianity which is quantitatively measured by the absolute value of kurtosis in the time-frequency domain. Then a signal sorting method based on correlation detection in time-frequency domain is proposed to obtain the target signal. Experiments on the real radar data with deception and suppression interference demonstrate that the TF-RobustICA can effectively suppress main-lobe interference, and separate the target echo.