Application of Subarray Averaging and Entropy Minimization Algorithm to Stepped-Frequency ISAR Autofocus

Subarray averaging and entropy minimization (SAEM) algorithm is applied to stepped-frequency ISAR autofocus to compensate for the phase error along the down-range due to high target speed and low pulse repetition frequency (PRF) of radar systems. In stepped-frequency radar systems, the phase error due to target motion in each burst make range profiles blur. Thus, the stepped-frequency ISAR often adopts a high PRF radar system in order to suppress the effect of target motion in each burst. However, it inevitably leads to loss of many advantages by adopting low PRF radars in stepped-frequency ISAR imaging. In order to take advantage of a low PRF radar in stepped-frequency ISAR imaging, this paper makes use of a method called SAEM (subarray averaging and entropy minimization) that uses a subarray averaging concept in conjunction with the entropy cost function in order to find the target´s motion parameters. Well-focused ISAR images also can be provided from the combination of the SAEM and a traditional autofocus algorithm, even when low PRF stepped-frequency waveform is used. The effectiveness of this method is illustrated and analyzed with simulated targets comprised of point scatters and measured Boeing-737 data.