ISAR Image Improvement Using STFT Kernel Width Optimization Based on Minimum Entropy Criterion

Nowadays, radar systems have many applications and radar imaging is one of the most important of these applications. inverse synthetic aperture radar (isar) is used to form an image from moving targets. conventional methods use fourier transform to retrieve doppler information. however, because of maneuvering of the target, the doppler spectrum becomes time-varying and the image is blurred. joint timefrequency transforms (jtft) like short-time fourier transform (stft) can resolve the doppler spectrum and reduce the image blurring. these transforms use some kernels for signal spectrum analysis. according to the uncertainty principle, the proper selection of this kernel and its parameters could affect the quality of the image. in this paper, using a conventional kernel for stft, i.e. gaussian kernel, we use minimum entropy criterion to optimize the kernel duration. simulation results show that this optimization can improve the constructed image compared with the fourier transform method.