Suppressive Interference Suppression for Airborne SAR Using BSS for Singular Value and Eigenvalue Decomposition Based on Information Entropy
Si Chen, Lin Yang, Yue Yuan, Xiaoxiong Li, Linsheng Hou, Shuning Zhang
Abstract
Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.