Siqian Zhang

Imaging of Downward-Looking Linear Array Three-Dimensional SAR Based on FFT-MUSIC

In this letter, a novel imaging algorithm of downward-looking linear array 3-D synthetic aperture radar (SAR) is presented. To improve the resolution in cross-track direction, a multiple-signal-classification algorithm has been used. However, the computational cost is unattractive. To cover the shortage, fast Fourier transform (FFT) is utilized to roughly focus the targets in cross-track direction in this letter. Then, the range of peak searching can be considerably reduced. In addition, the backscattering coefficient can be directly obtained by FFT. On the other hand, since the scattering centers are always correlated in a real SAR system, the estimated covariance matrix is singular. To address the problem, the nearby spatial smoothing method is proposed. The array vectors of the nearby range and azimuth units are used to reconstruct the estimated correlation matrix. The effective aperture of the array can also be kept.

Truncated SVD-Based Compressive Sensing for Downward-Looking Three-Dimensional SAR Imaging With Uniform/Nonuniform Linear Array

For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

Superresolution Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on Two-Dimensional Compressive Sensing

For downward-looking linear array 3-D synthetic aperture radar (SAR), the azimuth and cross-track resolution are unacceptable due to the length limitation of synthetic aperture and linear array. Hence, superresolution reconstruction algorithms are desired. Since the signal to be reconstructed is sparse on the 2-D azimuth-cross-track plane, it is quite suitable to apply the compressive sensing theory to obtain the images. The existed imaging algorithms for downward-looking linear array 3-D SAR are based on 1-D compressive sensing, which could bring the couple effect between different directions. To solve this problem, a novel 3-D imaging algorithm based on 2-D compressive sensing is proposed in this paper. Instead of converting the sparse reconstruction of 2-D matrix signals to the sparse reconstruction of 1-D vectors, the proposed algorithm directly reconstructs the 2-D sparse signals on overcomplete dictionaries with separable atoms. It not only provides superresolution performance, but also reduces the storage of data acquisition and processing. Furthermore, a definition of joint sparse sampling strategy is given to reconstruct the measurement matrices for further improving the computational efficiency of the imaging algorithm. Moreover, in order to investigate the limits of the proposed algorithm, the theory analysis of Cramér-Rao bound is derived and compared with the standard deviation. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity and the limits of the proposed algorithm.

Analytic estimation performance bounds of downward-looking linear array 3-D SAR imaging based on compressive sensing

For downward-looking linear array three-dimensional SAR, the resolution in cross-track direction is a curial problem. Hence, compressive sensing algorithm has been used to acquire the superresolution performance in cross-track direction. The limits of the proposed algorithm are investigated in this paper. How accurately can the scattering intensity of the scatterers be estimated? What is the closest separable distance of two scatterers at different levels of SNR? What is the influence of the acquisitions N on the resolution? For all of these questions, the theoretical analysis is given by Cramér-Rao Bound and numerical simulations are proven. The results can be considered as a fundamental bound on parameter estimates.

Adaptive and Fast Prescreening for SAR ATR via Change Detection Technique

Change detection is a process of identifying changes in the state of objects between the reference and test images. This letter presents a target prescreening method that employs the change detection technique for automatic target recognition in synthetic aperture radar (SAR) images. First, four translated versions of an original SAR image are generated, and the corresponding four likelihood ratio images are computed. Then, a robust threshold is derived from the ratio of the histogram at two adjacent gray-level values of the likelihood ratio images. Finally, the threshold is applied to perform the prescreening. The proposed method implements the procedure without any prior knowledge and overcomes the weak adaptability of traditional algorithms. Two different real X-band airborne SAR images acquired over Beijing are used to quantitatively and qualitatively demonstrate the effectiveness of the proposed method.