Minimum Entropy Iterative Algorithm for Phase Correction in ISAR Motion Compensation

The Minimum Entropy Iterative Algorithm is an efficient method for motion compensation in Inverse Synthetic Aperture Radar (ISAR) systems, primarily used to improve target image focus quality. During ISAR imaging, non-ideal target motion introduces phase errors that degrade image resolution. This algorithm corrects phase errors by optimizing entropy values, thereby enhancing imaging clarity.

The core concept of the algorithm involves iteratively adjusting phase compensation parameters to minimize image entropy. Entropy serves as an indicator of image focus quality - lower entropy values correspond to sharper, better-focused images. In practical implementation, optimization techniques such as gradient descent are typically employed to progressively approach optimal phase correction values through iterative parameter updates.

Compared to traditional methods, the Minimum Entropy Iterative Algorithm demonstrates superior stability under complex motion conditions, making it suitable for high-precision ISAR imaging requirements. This method holds significant application value in military reconnaissance and remote sensing monitoring, where it can be implemented using numerical optimization libraries to handle complex phase correction scenarios efficiently.