| Linear frequency modulation(LFM)signals,as the transmitting and receiving signals of broadband millimeter wave radar,have the advantages of high resolution and good anti-interference,and have been widely used in the field of radar imaging.Using the traditional Nyquist sampling method to obtain discrete samples of radar echo signals without distortion requires a sampling frequency not less than twice the highest frequency of the signal.Due to the limitation of sampling frequency,traditional sampling is no longer sufficient to meet the demand for undistorted sampling of broadband signals.The theory of compressed sensing(CS)breaks the limitation of traditional sampling frequency.According to the sparsity of the signal on a sparse basis,CS theory maps the signal from a high-dimensional space to a low-dimensional space through the observation matrix,and retains all the information of the original signal.Analog Information Converter(AIC)can perform CS sampling on analog signals based on CS theory.Compared with traditional sampling,the main challenge faced by CS is how to construct reconstruction algorithms to restore the original signal without distortion.Therefore,reconstruction algorithms have become a hot research topic in the field of CS.Taking noncooperative targets as the starting point,this paper proposes a linearly optimized sparse adaptive matching pursuit(LOSAMP)algorithm using the linearized step size optimization strategy by analyzing the defects of the step size selection strategy in the traditional sparse adaptive matching pursuit(SAMP)algorithm,and combines it with ISAR imaging technology,and verifies the recovery effect and imaging results of the LOSAMP algorithm through simulation.The main work of this thesis is as follows:1.In response to the shortcomings of the step size selection strategy in traditional SAMP algorithms,this paper proposes a linear optimization step size strategy for the LOSAMP algorithm,and compares the recovery effects of these reconstruction algorithms through MATLAB platform simulation,verifying the advantages of the LOSAMP algorithm in terms of computational speed.2.This article models the digital model of Random Demodulator(RD)and radar echo signal on the MATLAB simulation platform.The radar signal CS sampling is completed through RD,and the radar echo signal is restored using the LOSAMP algorithm.3.Based on the sparsity of LFM signals under pulse compression and fractional Fourier transform(FRFT),as well as the sparsity of target azimuth,a sparse basis for ISAR data was established.In the MATLAB simulation platform,the LOSAMP algorithm was combined with the constructed sparse basis to complete radar ISAR imaging of targets;And for sparse aperture ISAR data,compared with traditional Range Doppler(RD)imaging methods,the advantages of using CS for ISAR imaging were verified. |
