Resolution In Cross-track Direction Research Articles

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar (SAR), the three-dimensional (3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection (BP) and the data extraction method based on modified uniformly redundant arrays (MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing (CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally, by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

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.

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.

RESOLUTION ENHANCEMENT FOR LASAR 3D IMAGING VIA ℓ<sub>1</sub> REGULARIZATION AND SVA

Linear array SAR (LASAR) has been attracting more and more attention for its capability of obtaining three dimensional (3D) resolutions. However, the low resolution in cross track (CT) direction limited by the length of its linear antenna array has become the bottleneck of its practical applications. To overcome this problem, we present a novel algorithm based on sparse reconstruction (SR) to improve the resolution in CT direction. First, it establishes a 1D real-valued sparse model for LASAR, which deals with the 3D image column by column along CT direction in each equi-range slice. This enables it to handle large scenes. Second, it employs the spatially variant apodization (SVA) to filter bases of the measurement matrix. As a result, the cross coherence gets suppressed as well, and it is helpful to improve the performance of sparse reconstruction algorithms (SRAs). Third, we propose the resolution enhancement ability (REA), which provides a new idea to evaluate how many times the resolution could be improved. Experimental results validate that when the signal to noise ratio (SNR) is 30 dB, LASAR could usually obtain 2 times of resolution improvement in CT direction, while the proposed method further improves the REA by a factor about 2.5. Moreover, the 3D surface terrain simulation shows a great improvement for the digital elevation map (DEM) in resolution enhancement.