Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning
To estimate parameters of parameterized scattering center models is one of the basic methods for Synthetic Aperture Radar Advanced Information Retrieval(SAR AIR).Traditional Attributed Scattering Center(ASC)parameter estimation algorithms usually suffer from issues such as slow computation speed,high algorithm complexity,and high sensitivity to initial values of parameters.In this paper,a novel end-to-end framework for inverting ASC parameters from radar images based on unsupervised deep learning is proposed.Firstly,an autoencoder network structure is employed to effectively extract image features of targets,alleviating the difficulties solving directly caused by the complex non-convex optimization space and resolving the sensitivity to initial values.Secondly,the ASC model is embedded as a physical decoder to constrain the encoder output to correct ASC parameters.Finally,the end-to-end architecture are utlized to train and infer the model,achieving the purpose of reducing algorithm complexity and improving estimation speed.Through testing on simulated and measured data,experimental results indicate that the estimation error obtained on the SAR image test set with a resolution of 0.15 m is less than 0.1 m while the average processing time is 0.06 s for the inversion of one single scattering center,which demonstrate the effectiveness,efficiency,and robustness of the proposed approach.
万方数据
维普
