A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

Zisheng Wang,Conghui Qi,Zhuming Chen,Zhiqin Zhao,Haoquan Hu,Wei Yang

doi:10.3390/rs10020334

Abstract

We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN) to focus the distorted inverse synthetic aperture radar (ISAR) image. In this paper, a coefficient estimator based on the artificial neural network (ANN) is firstly developed to solve the time-consuming rotational motion compensation (RMC) polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs). Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

Highlights

Inverse synthetic aperture radar (ISAR) images are the two-dimensional projection of the target radar echo and can offer sufficient target signatures, such as the target’s movement and the detection for non-cooperative maneuvering targets [1]
Before employing the adaptive joint time-frequency algorithm to carry out the motion compensation, three basic assumptions are needed: (1) the target should be a rigid body during the coherent processing interval (CPI), i.e., the dominant scatterer contains motion characteristics of the whole target; (2) the target motion is confined to a 2-D plain, i.e., the target has only one fixed rotational axis in order to simplify the model and discussion of the rotational motion compensation (RMC) problem; (3) the coarse motion compensation, such as the range alignment, has been finished
Images processed by adaptive joint time frequency (AJTF)-polynomial phase transform (PPT) and AJTF-particle swarm optimization (PSO) are shown in Figure 4d,e, respectively as a comparison

Summary

Introduction

Inverse synthetic aperture radar (ISAR) images are the two-dimensional projection of the target radar echo and can offer sufficient target signatures, such as the target’s movement and the detection for non-cooperative maneuvering targets [1]. Besides the translational motion part, movements of targets consist of other complex disturbances, such as rolling, pitching and yawing, causing smearing and distortion of ISAR images [2]. The motion compensation algorithm needs to be considered as the first step of ISAR images. Even though many methods have been successfully applied to remove the translational motion by the translational motion compensation (TMC) [3,4,5], the remaining unexpected rotational motions of target still induces a bad deterioration in ISAR images. The final rotational motion compensation (RMC) procedure matters a lot

Methods

Results

Discussion

Conclusion