A reassigned bilinear transformation and Gaussian kernel function-based approach for detecting weak targets in sea clutter

Abstract

ABSTRACTThis article proposes an efficient approach for weak target detection in sea clutter by using reassigned bilinear transformation based on the Gaussian kernel function (KF). This approach, which makes no assumptions on the sea clutter, mainly incorporates three steps: time-frequency analysis, feature extraction, and pattern classification. Bilinear transformation is utilized to transform sea clutter data into a time-frequency image, and the suitable Gaussian KF is applied to restrain the cross-term interference. Subsequently, to obtain a better performance in focus-ability, readability, and time-frequency resolution, we reassign the time-frequency distribution. In addition, we employ its marginal distribution of frequency (MARF) as classification feature vectors to decrease the dimensions of time-frequency features. Finally, a multilayered feed-forward neural network (NN) is selected as a classifier in the pattern classification process. Detection performances using the IPIX radar experimental sea clutter data are compared among the proposed method, the method based on continuous wavelet transform, and the conventional constant false alarm rate (CFAR) method based on spectral properties. A comparison of the results shows that the proposed method is more effective and efficient than the other two methods for weak target detection in sea clutter.