基于等效相位中心近似与改进SLIM算法的稀疏阵列快速成像算法
投稿时间:2019-10-18  修订日期:2019-11-18  点此下载全文
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作者单位E-mail
孟祥新 博微太赫兹信息科技有限公司 15856969462@163.com 
武帅 博微太赫兹信息科技有限公司 demonmeng@163.com 
涂昊 博微太赫兹信息科技有限公司  
柳桃荣 博微太赫兹信息科技有限公司  
靳学明 博微太赫兹信息科技有限公司  
中文摘要:一种应用于毫米波稀疏阵列成像的基于频率域成像算法和压缩感知技术相结合的成像算法在本文被提出。算法包含两个主要步骤,首先采用等效相位中心近似原理,将快速傅里叶变换成像算法用于周边形阵列,由于等效相位中心近似引入的残余相位误差无法在近距离成像应用中被完全补偿,因此在第二个步骤中,提出基于压缩感知技术的基于迭代最小化的稀疏学习(SLIM)的改进算法用于重聚焦初始图像。通过等效相位中心近似原理和改进的SLIM算法的结合,所提算法具备更高的计算效率、提升了图像质量、相比于传统的SLIM算法具备更少的迭代次数。仿真结果验证了所提算法的有效性。
中文关键词:压缩感知  傅里叶变换成像算法  稀疏阵列  改进的SLIM算法
 
A Fast Imaging Algorithm for Sparse Array Imaging Based on PCA and Modified SLIM Methods
Abstract:An algorithm combining frequency domain imaging algorithm and compressive sensing (CS) framework is proposed in here for millimeter-wave multi-static sparse array imaging. The algorithm consists of two major steps. Firstly, the typical fast Fourier transform (FFT) algorithm used in square boundary array with phase center approximation (PCA) is carried out. However the residual phase error introduced by the PCA at close range cannot be compensated completely, so in the second step, the modified sparse learning via iterative minimization (SLIM) algorithm which is in the CS framework is introduced to refocus the initial images. By combining PCA and the modified SLIM algorithm, the proposed algorithm reaches a better computational efficiency, improves the image quality, and alleviates the requirement for iterations of the original SLIM algorithm. Simulation results verify the effectiveness of this algorithm.
keywords:compression sensing  fft imaging algorithm  sparse array  the modified slim algorithm
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