Distributed ISAR Two-dimensional Imaging of Moving Target with Nonorthogonal Waveforms
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摘要: 分布式ISAR成像中若发射波形非理想正交，传统的匹配滤波方法难以得到理想的距离像，进而会影响方位成像效果。基于稀疏的方法可代替匹配滤波进行距离像分离。该文在给出单次快拍时距离像稀疏表示模型后，通过调整收发阵元的时延，可以使多接收阵元的距离像具备联合块稀疏特性。随后采用1阶负指数函数(SOONE) 实现多观测向量联合块稀疏算法(MMV-JBlock)提升稀疏重构的效果。多次快拍时，在每个快拍时刻采取MMV-JBlock方法分离距离像。在对齐多通道距离像后，对方位相位中非关心方向运动和误差项进行补偿，最后也采用稀疏方法得到目标的方位像。仿真验证了在不同稀疏度和不同信噪比下所提算法的重构性能，并仿真实现了分布式ISAR对运动目标的成像，验证了所提方法的有效性。Abstract: In distributed Inverse Synthetic Aperture Radar (ISAR) imaging, if the transmitted waveforms are nonorthogonal, it is difficult to obtain the ideal range image by the traditional matched filtering method, which will affect the azimuth imaging effect. Sparse-based method can replace matched filtering in range profile separation. In this paper, after describing the sparse representation model of range image in a single snapshot, by adjusting the delay of the transmitting and receiving sensors, the range image with multiple receiving sensors can have joint-block sparse characteristics. Then, a Multiple Measurement Vectors Joint Block (MMV-JBlock) algorithm is constructed using Sequential Order One Negative Exponential (SOONE) function to improve the effect of sparse reconstruction. For multiple snapshots, the MMV-JBlock method is used to separate the range image at each snapshot firstly. After aligning the multi-channel range images, the uninterested directional motion and error items in the azimuth phase are compensated. Finally, the sparse method is used to obtain the target azimuth image. The simulation verifies the reconstruction performance of the proposed algorithm under different sparsity and different signal-to-noise ratios, and achieves the imaging of moving targets by distributed ISAR, which validates the effectiveness of the proposed method.
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