ABSTRACT Introducing compressed sensing theory into the millimeter-wave near-field holographic imaging algorithm, it can break the Nyquist sampling limit, reconstruct the compressed echo signal, and invert the target image. In the reconstruction process, there are defects such as missing target key information, excessive invalid search volume and so on. Aiming at this problem, an adaptive multi-extreme particle swarm optimization (AMPSO) algorithm is proposed. Its advantages are that it can retain more target information, search for more extreme values, and improve the convergence speed. At the same time, the search probability in the strong scattering area is also increased, the search time is avoided in the noise area, and the number of extreme points is adjusted on a global scale. The effectiveness of the algorithm is verified by simulation and actual measurement of multiple types of targets under different experimental conditions.

中文翻译：

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基于AMPSO搜索算法的MMW压缩感知目标重建

摘要 将压缩感知理论引入毫米波近场全息成像算法，可以突破奈奎斯特采样极限，重构压缩回波信号，实现目标图像反演。在重构过程中存在目标关键信息缺失、无效搜索量过大等缺陷。针对这一问题，提出了一种自适应多极值粒子群优化（AMPSO）算法。它的优点是可以保留更多的目标信息，搜索更多的极值，提高收敛速度。同时，也增加了在强散射区的搜索概率，避免了在噪声区的搜索时间，在全局范围内调整极值点的数量。