Imaging and parameter estimation of moving targets in airborne single-antenna high-resolution synthetic aperture radar (SAR) system is affected by motion errors, which cause defocusing and dislocation of moving targets. Motion errors derive from platform flight deviation and unknown target velocity and estimating all motion errors once is difficult because both aspects influence each other, especially in unmanned aerial vehicle (UAV) systems. We find that the platform flight deviation has the same effect on stationary targets and moving ones. Exploiting this similarity, we propose a novel ground moving target motion compensation method. Platform flight errors are extracted from stationary targets by autofocus algorithms and compensated for moving targets. And then Hough transform (HT) and Map-Drift (MD) technique is used to estimate the radial velocity and the along-track velocity, and range cell migration correction (RCMC) is completed by estimated velocities. Furthermore, PGA technique is adopted to estimate and correct residual phase errors. The effectiveness of the proposed algorithm is validated by the real data.
The phase gradient autofocus (PGA) technique is applied over a wide range of imagery and phase error corrections for synthetic aperture radar (SAR). In this paper, we propose an improved PGA method to increase the phase error estimation accuracy by selectively increasing the pool of quality synchronization sources. This improved method operates mainly in three steps. Firstly, after compressing the deramped data by taking FFT, strong targets can be identified and selected out according to the pixel magnitudes over the two-dimensional (2-D) image. Secondly, sort the selected targets by computing their contrast in the azimuth direction and select out the ones with good contrast. Thirdly, sort the selected targets by computing their peak signal-to-noise (PSNR) in the azimuth direction and select out the good quality targets. After these three times filtering, those selected scatterers are optimal in terms of having sufficient signalto-noise energy with negligible impulse response interference to the neighboring targets and clutter scatterers. With these selected quality scatterers, the proposed modified PGA method can achieve a better focusing performance. Advantages and effectiveness of the proposed algorithm are verified on the real stripmap SAR data.
In synthetic aperture radar (SAR), many of the biased baseband Doppler centroid estimates are caused by partial exposures of bright targets. To avoid this problem, this paper proposes a new estimation algorithm to remove the effects of partial exposures under non-homogeneous environment. This algorithm is implemented by three steps. Firstly, concentrate the energy of each target into a few pixels through azimuthal compression using frequency domain matched filter. Then, extract the homogeneous environment through the spectral distortion and contrast, and eliminate the strong interference target. Finally, estimate the centroid of the averaged extracted power spectrum using a circular convolution method. Theoretical analysis and experimental results show that the proposed method can effectively eliminate the interference targets and accurately find the Doppler centroid under non-homogeneous environment with strong interference. Moreover, the operations of the proposed algorithm need no iterations, thus the computational load is greatly reduced compared with the traditional iterative methods.
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