Ultra-wideband Radar Inverse Imaging Based On Compressed Sensing And Truncated Singular Value Decomposition

With the increasingly demanding for high-resolution radar image, signalbandwidth and antenna aperture of radar system are increasing, resulting the amountof data and the acquisition time are also growing, which bring great challenge forradar system’s hardware and signal follow-up processing; In addition, in traditionalsubspace-based ultra-wideband radar inverse scattering imaging, because of highcomputational complexity in solving inverse scattering problem, it is difficult to meetthe demand for the practical application of the imaging system. On that account, thispaper propose a ultra-wideband radar inverse imaging method based on compressedsensing and truncated singular value decomposition, designing to use compressedsensing imaging method compensate the problem of imaging time too long, and usetruncated singular value decomposition inverse imaging method to compensate theproblem of imaging resolution is not high.In this paper, firstly, introduce the extraction of data in ultra-wideband radar andsome preprocessing methods, then based on electromagnetic scattering model derivedultra-wideband radar inverse imaging method; Secondly, study compressed sensingtheory applied to ultra-wideband radar imaging, theoretical derivation compressedsensing imaging algorithm, then combined with simulation data validation thefeasibility of algorithms, and from the point of sparsity, measure dimension andanti-noise performance, analyze four kinds of matching reconstruction algorithmperformance (orthogonal matching pursuit, stagewise orthogonal matching pursuit,regularized orthogonal matching pursuit and compressive sampling matching pursuit);Finally, we propose a ultra-wideband radar inverse imaging method based oncompressed sensing and truncated singular value decomposition, then comparison ofimaging results with compressed sensing and truncated singular value decomposition,analyzed the advantage of this method.The experimental results showed that: compressed sensing matchingreconstruction algorithm can use little measurements data accurately detecting thetarget position, and have better anti-noise performance, comparative results show that regularized orthogonal matching algorithm overall performance is more superior; weproposed inverse imaging method based on compressed sensing and truncatedsingular value decomposition compared with compressed sensing method have ahigher image resolution, and imaging results can better reflect the contrast functionasymptotic distribution; Compared with truncated singular value decompositionmethod, imaging time is reduced by65.34%, greatly speeding up the imaging speed.