Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on the Two-Dimensional Mismatch Compensation

Abstract

For downward-looking linear array (DLLA) 3-D synthetic aperture radar (SAR), it is necessary to realize the super-resolution in both azimuth and cross-track direction due to the limited lengths of the synthetic aperture and the linear array. As all the scatterers are assumed on the uniform grids, the cross-track super-resolution can be achieved by 1-D compressed sensing. In the real imaging system, however, the gridding error should be considered because the biased scatterers lead to the mismatch of the measurement matrix and affect the imaging performance. The 1-D mismatch in cross-track direction has been solved by atomic norm minimization and off-grid sparse Bayesian inference. With the development of the super-resolution methods, 2-D super-resolution in both azimuth and cross-track direction is realized by the 2-D CS algorithms. To solve the 2-D mismatch problem, a novel 2-D mismatch compensation method for DLLA 3-D SAR is proposed. Instead of converting the 2-D matrix signals to the 1-D vectors, the proposed method directly processes the 2-D mismatch with 2-D joint model. Furthermore, the 2-D joint model with 2-D mismatch is simplified as a normal sparse linear model, which is suitable for most of the CS reconstruction algorithms. It can not only provide better reconstruction performance, but also reduce the memory cost and computation load. Finally, simulation experiments are shown to demonstrate the validity of the proposed method.

A Novel Downward‐Looking Linear Array SAR Imaging Method Based on Multiple Measurement Vector Model withL2,1‐Norm

“…The simulations of point targets and distributed targets are given to verify the performance of the proposed algorithm. The simulations of point targets and the experiments of distributed targets utilize the same system parameters, which are chosen according to [10,19,25] and shown in Table 1.…”

“…is obtained by the 3DRD method, in which the CT compression is realized by matched filtering so that the middle two points are overlapped. For the experiment of distributed 3-D targets, the above method to generate the simulated echo signal has been used in [15][16][17][18][19]25]. According to the parameters in Table 2, the Rayleigh resolution is 1.5 m. So the interval space between scatters is set as 1 m. The scatter number in the simulation is N p N q = 400 × 400.…”

“…The range compression of the proposed method is the same as that of the traditional methods. The details of the range compression in DLLA SAR can be found in [25]. Firstly, the echo signal should multiply by the range-matched filter function and the range migration correction functions in the frequency domain.…”

“…where JðGÞ is the objective function and λ is the regularization parameter. According to the regularization theory [25], the regularization parameter can be λ = 2β 2 , where β is the variance of the reconstructed error. To solve Equation ( 14), the iterative formula of step (3) in Algorithm 1 should be modified.…”

“…Finally, the AT compression is the same as that of the traditional methods. It is realized by the matched filtering in the AT domain, and the detailed 6 Journal of Sensors information can be obtained in [13,25]. The 3-D imaging result is…”