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.

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.

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.

With the inverse synthetic aperture radar (ISAR) imaging model, targets should move smoothly during the coherent processing interval (CPI). Since the CPI is quite long, fluctuations of a target's velocity and gesture will deteriorate image quality. This paper presents a multiple-input-multiple-output (MIMO)-ISAR imaging method by combining MIMO techniques and ISAR imaging theory. By using a specialM-transmitterN-receiver linear array, a group ofMorthogonal phase-code modulation signals with identical bandwidth and center frequency is transmitted. With a matched filter set, every target response corresponding to the orthogonal signals can be isolated at each receiving channel, and range compression is completed simultaneously. Based on phase center approximation theory, the minimum entropy criterion is used to rearrange the echo data after the target's velocity has been estimated, and then, the azimuth imaging will finally finish. The analysis of imaging and simulation results show that the minimum CPI of the MIMO-ISAR imaging method is 1/MNof the conventional ISAR imaging method under the same azimuth-resolution condition. It means that most flying targets can satisfy the condition that targets should move smoothly during CPI; therefore, the applicability and the quality of ISAR imaging will be improved.