非均匀转动空间目标天基逆合成孔径激光雷达成像

由于没有大气的影响，天基逆合成孔径激光雷达(ISAL)可在远距离上获取空间目标的高分辨率图像，因此具有重要的应用前景。建立了空间目标天基ISAL成像的方位向回波信号模型，在目标非均匀转动二阶近似下，即匀加速转动时，ISAL方位向回波信号可近似为调频斜率和起始频率各异的多分量线性调频(MLFM)信号，采用传统的FFT方法难以实现方位图像聚焦。提出了一种基于Radon-Wigner变换的方位快速成像算法，利用Radon-Wigner变换并结合逐次消去法，在每个距离单元对回波中的MLFM信号由强至弱逐个进行估计，将估计获取的峰值点直接提取并线性叠加，即得到该距离单元的方位像。通过对所有距离单元进行上述处理，即可获得二维ISAL图像。由于该方法在估计出MLFM信号后无需再进行瞬时多普勒成像处理，因此减少了处理流程，算法效率大幅提高。仿真试验表明，相比传统的距离瞬时多普勒成像算法，该算法平均处理时间从222.66 s降低至23.51 s，在低信噪比情况下图像中目标轮廓更显著，更易于检测识别。

Spaceborne inverse synthetic aperture lidar imaging of nonuniformly rotating orbit object

Without the turbulence of atmosphere, spaceborne inverse synthetic aperture lidar (ISAL) can obtain high-resolution images of orbit objects at a long distance. Therefore, ISAL plays an important role in spaceborne imaging. The cross-range echo signal model of spaceborne ISAL is established. Generally, the motion of an orbital object is considered a second-order rotation after translational compensation, i.e. uniformly accelerated rotation. Under this condition, the ISAL cross-range echo signals can be equivalent to multicomponent linear frequency modulation (LFM) signals with various chirp rate and initial frequency. Therefore, it is difficult to obtain a well-focused image when using the traditional FFT method. A fast cross-range imaging algorithm based on Radon-Wigner transform is proposed. The Radon-Wigner transform combined with a successive elimination procedure are performed to estimate and separate the cross-range multicomponent LFM signals one by one from strong to weak, in each range unit. After prominent peak points are estimated and extracted in all range units, rearrangement is performed by linear superposition and a focused 2D ISAL image can be obtained. The proposed method avoids to obtain signal parameters and conduct instantaneous Doppler imaging, which is simplified and greatly improved in efficiency. Simulation results show that, compared with the traditional range instantaneous Doppler imaging algorithm, the average processing time of the proposed algorithm is reduced from 222.66 s to 23.51 s. In the case of low signal-to-noise ratio, the target contour in the image is more significant and easier to detect and recognize.