During the observation of micromotion targets in space, inverse synthetic aperture radar usually obtains the narrowband and wideband echoes simultaneously. In order to exploit their rich information in target electromagnetic scattering, shape, structure, and motion, this article proposes a recognition method of space micromotion targets based on decision fusion. The proposed method extracts physical features from the radar cross section and the joint time–frequency distribution from narrowband echoes, while extracts the data features from high-resolution range profiles and range-instantaneous-Doppler image by convolution neural network. Finally, particle swarm optimization is adopted to decision-level fusion so as to realize high-precision recognition. The recognition results of electromagnetic simulated data under various conditions have demonstrated the effectiveness and robustness of the proposed method.

中文翻译：

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微动空间目标融合识别

在对空间微动目标的观测过程中，逆合成孔径雷达通常同时获取窄带和宽带回波。为了充分利用其在目标电磁散射、形状、结构和运动方面的丰富信息，本文提出了一种基于决策融合的空间微动目标识别方法。该方法从雷达截面中提取物理特征，从窄带回波中提取联合时频分布，同时通过卷积神经网络从高分辨率距离剖面和距离瞬时多普勒图像中提取数据特征。最后采用粒子群优化进行决策级融合，实现高精度识别。