Inverse synthetic aperture lidar imaging and compensation in slant atmospheric turbulence with phase gradient algorithm compensation

To systematically study the influence of slant atmospheric turbulence on the inverse synthetic aperture lidar (ISAL) imaging, the atmospheric random phase screen with the modified von Kármán spectrum was numerically simulated via the fast Fourier transform spectral inversion method. Intensity phase distribution and brightness images of the Gaussian beam slant transmission were simulated under different turbulence intensities. Using the Kirchhoff approximation and convolution back-projection algorithm, ISAL images of rough surface (rough square and rough circular plates), rough graphics and rough body with different roughnesses under various turbulence intensities were obtained. The analyses showed that these ISAL images of rough targets can be resolved under weak turbulence but not under moderately strong turbulence. Aiming at the reduction of the resolution of the rough target ISAL images caused by atmospheric turbulence, the phase gradient autofocus (PGA) algorithm was used to improve image focusing and distinguish the shapes of rough targets.