Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.

中文翻译：

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基于多个测量矢量的圆锥形目标双基地雷达重合成像

双基地雷达成像可以克服单基地雷达成像的局限性，获得丰富的目标特征信息。因此，紧随其后。与双基地逆合成孔径雷达（Bi-ISAR）成像不同，双基地雷达重合成像（Bi-RCI）为双基地雷达成像技术提供了新的思路。为了实现Bi-RCI的相干处理并提高成像性能，提出了一种基于多个测量矢量（MMV）的旋转圆锥形目标Bi-RCI。基于混合模式信号，建立了MMV参数模型，并提出了测量数的粗略选择。最后，提出了一种改进的稀疏贝叶斯学习算法来重建目标图像。仿真结果证明了该方法的有效性和优越性。