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Open AccessArticle

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

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2018, 10(2), 334; https://doi.org/10.3390/rs10020334
Received: 27 January 2018 / Revised: 8 February 2018 / Accepted: 10 February 2018 / Published: 23 February 2018
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: ISAR image; adaptive joint time-frequency; rotational motion compensation; neural network ISAR image; adaptive joint time-frequency; rotational motion compensation; neural network
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MDPI and ACS Style

Wang, Z.; Yang, W.; Chen, Z.; Zhao, Z.; Hu, H.; Qi, C. A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation. Remote Sens. 2018, 10, 334.

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