Research On Cognitive Microwave Imaging And Error Correction Method Based On Metasurface Aperture

As an important tool to expand human vision and enhance human’s ability to understand the physical world,the imaging system has been widely concerned for a long time.Compared with optical imaging,microwave imaging is an active detection technology,which is less affected by weather and environment and can work all-weather.With the development of radar technology,the ability of radar to acquire targets is also continuously improved.Radar imaging technology has been continuously developed and widely used in the field of national defense and social life.Radar microwave imaging includes synthetic aperture microwave imaging and real aperture microwave imaging.Metasurface aperture radar is a real aperture radar imaging system,and computational microwave imaging of frequency-diverse metasurfaces(FDM)is an emerging technology.Metasurface aperture antenna produces a unique spatial control capability for electromagnetic waves through the special design of antenna,that is,changing the physical structure and layout of the aperture resonance unit.Then a radiation field with random distribution can be generated in space for detection of scene under the excitation of different frequency signals.Metasurface aperture imaging radar will have broad application prospects in the field of security detection,planetary detector environmental perception and unmanned combat platforms in the future due to its advantages of system with small form-factor,low cost,low complexity and all-weather.The main research contents of this paper are the basic theory of metasurface aperture radar imaging,and two methods were proposed to improve the imaging quality of FDM imaging system based on the principle of FDM imaging system and hardware equipment conditions.One is to optimize the energy of the patterns excited at different frequencies at the transmitter.The other is to correct the amplitude and phase errors of the channel.At the same time,the whole working environment of the metasurface aperture radar imaging system was introduced,and the measured data was simulated and analyzed based on the algorithm proposed in this paper.The specific research contents can be summarized in the following aspects:Firstly,this paper studied the basic theory of metasurface aperture radar imaging,analyzed the influence of metamaterial elements on the effective measurement mode number of metasurface aperture antenna and the frequency sensitivity characteristics of aperture resonance unit,researched on the generation mechanism of random radiation field of aperture antenna and the imaging mathematical model.Meanwhile,this paper analyzed whether the compressed sensing theory can be applied to the metasurface aperture imaging system.Second,this paper solved the transimmited waveform of imaging system optimization problem based on information theory for enhancement of imaging quality.Two waveform optimization methods were proposed under different conditions for the FDM imaging system.The first waveform optimization algorithm is maximizing the mutual information between the target and the measurement data under the condition that the transmission energy is constant,and the second waveform optimization algorithm is minimizing the transmission energy when the mutual information is not less than a certain value.The performance of the proposed waveform optimization method was evaluated by data collected from a self-built experimental FDM imaging system.According to the experimental results,the waveform optimization method proposed in this paper can improve the imaging quality or imaging efficiency of the FDM imaging system.Finally,this paper solved the channels of imaging system amplitude and phase error correction problem based on sparse Bayesian.FDM imaging can be thought of as microwave compressed sensing imaging,and then the target was recovered by a sparse reconstruction algorithm.However,the measurement matrix will be affected by the amplitude and phase errors within the channel,resulting in the degradation of the image quality.In the FDM imaging system,it can be known that the target signal is sparse,amplitude and phase error are mainly distributed at low frequency.Thus,the probability model of signal and error can be assumed to be sparse in a domain based on the some known prior information.This paper proposed an imaging algorithm due to these models under the framework of sparse Bayesian learning,which used Bayesian inference to estimate and calibrate the amplitude and phase errors of the transceiver channels.The accuracy of reconstructed images are improved through joint iterative estimation of error and signal.According to the experimental results,the method proposed in this paper can improve the image quality.