Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms.