A powerful method based on artificial bee colony algorithm for translational motion compensation of ISAR image

Abstract

Inverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two‐dimensional spatial distributions of radar cross‐section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2691–2698, 2014

A translational motion compensation technique for inverse synthetic aperture radar images using multi‐objective particle swarm optimization algorithm

“…Alternative methods given in the literature for the solutions of MC problems are the meta‐heuristic optimization algorithms such as particle swarm optimization (PSO) with island model (PSOI), artificial bee colony (ABC), genetic algorithm (GA) and differential evolution (DE) . In all these studies, the MC was performed with a single‐objective approach. However, the problem of minimizing entropy and maximizing the contrast requires multi‐objective consideration for better optimization.…”

“…Then, the optimal solution providing the clearest image is chosen from the Pareto front set, and compensated ISAR images without the motion effects are achieved. The performance of the MOPSO technique is demonstrated via a comparison with four studies previously presented in Reference through the well‐known algorithms of ABC, DE, PSO, and PSOI. The results show that the MOPSO technique shows better compensation performance than the single‐objective methods and construct the clearest ISAR image for all scenarios.…”

Non-Stationary Platform Inverse Synthetic Aperture Radar Maneuvering Target Imaging Based on Phase Retrieval

“…For example, the maximum probability estimate [11] shows good performance when performing motion parameter estimation, but the amount of calculation of the method is large because it is necessary to search for the estimated values at multiple dimensions. An ISAR imaging motion compensation technology based on parameter estimation is studied in [12,13,14]. References [15,16,17] proposed a cross-correlation method with a small scope of measuring velocity and a minimum entropy method with a large computational cost.…”

Isar rotational motion compensation algorithm using polynomial phase transform

“…Ideally, the desired target's motion is uniform rotational motion without translational motion, under which simple range‐Doppler (RD) algorithm forms a two‐dimensional (2D) ISAR image from radar data collected over a given coherent processing interval (CPI). Otherwise, a motion compensation technique should be considered as an intermediate step to form a focused ISAR image. Because translational motion of the target is the undesired motion component, it must be removed using reliable translational motion compensation (TMC) methods .…”

“…Otherwise, a motion compensation technique should be considered as an intermediate step to form a focused ISAR image. Because translational motion of the target is the undesired motion component, it must be removed using reliable translational motion compensation (TMC) methods . In contrast, rotational motion that yields the change in the aspect angle of the target from the radar line of sight is essential to separate scatterers in the cross‐range direction.…”