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

Zisheng Wang, Wei Yang, Zhuming Chen, Zhiqin Zhao, Haoquan Hu, Conghui Qi
2018 Remote Sensing  
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,
more » ... e 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. time-frequency analysis [9, 10] . In these works, authors carefully choose a short coherent processing interval using the time-frequency analysis. During the selected CPI, the motion of target is stable and can be regarded as constant. It dramatically reduces the potential distortion and shows a good result of the ISAR. This technique, however, does not make full use of radar signal, resulting in the decreasing of the cross-range resolution. In order to overcome disadvantages of the time-frequency analysis, some researchers introduce the theory of compressed sensing into the ISAR images reconstruction [11] [12] [13] [14] , especially for removing the non-uniform rotational motion. These works construct a parametric sparse representation model for the ISAR signal matrix, where the basis matrix is constructed according to the target rotation rate. Some works show good RMC results for an aircraft with rotational acceleration [11] [12] [13] . In addition, some works also use the sparse distribution of scatterers to directly reconstruct a high resolution ISAR image [15-17] Furthermore, another motion compensation approach with the cubic-order processing procedure [18] is also an option. The compressed sensing and RID offer an excellent solution for the post processing of ISAR imaging, it, however, is an extremely time-consuming algorithm which is not suitable for the real-time application. The adaptive joint time frequency (AJTF) is comprehensively discussed solving the RMC problem [19] , in which authors proposed a compensation method based on the estimation of polynomial phase coefficients of the cross-range signal on a special range cell which contains the dominant scatterer of the target. It is a panacea of this problem, if enough coefficients are estimated. However, the original AJTF algorithm needs the exhaustive searching to optimize the cost function, which obviously is time-consuming, so, the particle swarm optimization (PSO) and the genetic algorithm (GA) have been used as an improvement of tje AJTF algorithm to reduce the computational complexity [20] . However, when facing huge scene, the PSO and GA are still not competent, thus researchers focus on direct calculation method. In 2016, the polynomial phase coefficients of AJTF are estimated by polynomial phase transform (PPT) introduced by [21]. PPT dramatically reduces the time-consumption, however, it still has possibility to further improve. The process of estimation is, essentially, a parameter estimator for a sophisticated input signal. Due to the complexity of the problem and the number of parameters, traditional ways of signal processing cannot get an accurate estimation. Fortunately, with the development of computational capacity, the neural network (NN) becomes a possible solution by the automatically machine learning for difficult estimation problems. Scientists have proven that multi-layer feed-forward networks, with even only one hidden layer, can approximate any function and provide any desired accuracy [22] . The most common utility of artificial neural network (ANN) is directly learning top-layer features, such as the detection of buildings [23] and airport [24] in the image classification problem. Some works, instead of directly recognition, use ANN estimator to analyze parameters, like the frequency of the signal [25, 26] , which can be used as inputs for traditional processing methods. It gives us a new way to improve the AJTF, because the ANN has been proved to be an operative estimator from those previous works. Different from previous works of the RMC problem, a coefficient estimator based on the ANN is developed to replace the searching processes of the AJTF, like the PSO or the GA, in the RMC problem. The training method, cost function and structure of the ANN are comprehensively discussed. Meanwhile, since the lack of enough preliminary data makes the training more challenging in the ISAR problem, we originally propose a method to generate training dataset by the ISAR signal model with randomly chosen motion characteristics. Prediction results of the ANN estimator is used to directly compensate distorted ISAR images. Under some circumstances of the bad ISAR image quality, it can provide a more accurate and narrow initial searching range for the AJTF with PSO (AJTF-PSO). The proposed method is called as the adaptive joint time frequency algorithm combined with the neural network (AJTF-NN). Finally, ideal point scatterers and an aircraft model with complex motion are used to verify the validity of the proposed method. Simulation results show that the proposed method is much more faster than other prevalent improved searching methods. It can be even up to 424 times than the AJTF-PSO, and 2.07 times than the AJTF with PPT (AJTF-PPT). The proposed
doi:10.3390/rs10020334 fatcat:at7j6qfrwvgrzcdjcgos43h3n4