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A novel measurement matrix optimization method for radar sparse imaging with OFDM-LFM signals

机译:OFDM-LFM信号的雷达稀疏成像测量矩阵优化新方法

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Compressed Sensing (CS) has been widely used in radar imaging field to reduce the data amount. The measurement matrix has direct effect on the degree of dimension reduction and the quality of target image. However, the measurement matrix is usually chosen as random Gaussian matrix or local Fourier matrix, and the influence from target characteristics to the measurement matrix optimization has not been considered. In this paper, focuses on the OFDM-LFM signals, a novel measurement matrix optimization method for radar sparse imaging is proposed. In this method, genetic algorithm is used to implement the measurement matrix optimization by equaling the measurement matrix to the chromosome. And then the satisfied imaging result can be achieved with minimal measurement dimension by using the obtained optimal measurement matrix. Some simulation results illustrate the effectiveness of the proposed method.
机译:压缩传感(CS)已被广泛用于雷达成像领域,以减少数据量。测量矩阵直接影响尺寸减小的程度和目标图像的质量。然而,通常将测量矩阵选择为随机高斯矩阵或局部傅立叶矩阵,并且尚未考虑目标特性对测量矩阵优化的影响。本文针对OFDM-LFM信号,提出了一种新的雷达稀疏成像测量矩阵优化方法。在这种方法中,遗传算法用于通过使测量矩阵等于染色体来实现测量矩阵优化。然后,通过使用获得的最佳测量矩阵,可以以最小的测量尺寸获得满意的成像结果。仿真结果表明了该方法的有效性。

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