In airborne case, synthetic aperture radar images generally suffer from the deterioration because of the unknown phase error caused by unstable platform and atmosphere perturbation. To obtain the phase error, a novel autofocus algorithm referred to as blind homomorphic deconvolution autofocus algorithm is proposed in this study. In this method, a wavelet scaling function is used to construct a smooth subspace that is orthogonal to noise subspace. Then, the phase error can be separated and reconstructed by the generated smooth subspace based on the differences of the smoothness properties between phase error and image reflectivity. Compared with the traditional autofocus methods, the proposed method does not require an iterative estimation. Thus, the computational complexity can be significantly reduced. Simulation and real data processing results validate the effectiveness of the proposed method.

The back-projection algorithm (BPA) is a useful technique for synthetic aperture radar (SAR) imaging. The fast factorised BPA (FFBPA) recursively partitions the back-projection integral, thus significantly reducing the overall computation complexity corresponding to the improvement obtained by the FFT algorithm compared with the direct implementation of the discrete Fourier transform. In this study, the authors propose a new fast method, termed as the factorised polar-format BPA (FPFBPA), which combines the polar format algorithm and the factorised back-projection concept. It is demonstrated that, when the first-stage subaperture in the FPFBPA contains more than 3 pulses, the proposed method further reduces the computation complexity comparing with FFBPA, provided that the same interpolation method is used. The proposed algorithm is also capable of processing curved orbit and multi-mode SAR data. The effectiveness of the proposed algorithm is verified by the processed results using measured airborne data.

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches.

The compact polarimetric (CP) synthetic aperture radar (SAR) data is directly decomposed into three canonical components involving surface, double-bounce and volume, which avoids the approximated quad-polarimetric reconstruction based on several promising assumptions. Through several algebraic operations, the three-component decomposition under CP modes (π/4 mode and right circular transmit, linear receive (CTLR) mode) is deduced. With the experiments by using two classical data sets – San Francisco data from AIRSAR system and Oberpfaffenhofen data from ESAR system, the feasibility and flexibility of the CP decomposition are validated. Also, it has been found that the use of CP data can be further extended into many other polarimetric applications, and the three-component decomposition can achieve a better result with CTLR mode than with π/4 mode.

An unsupervised classification method based on find of density peaks(FDP) is proposed for the polarimetric synthetic aperture radar (POLSAR) image. For the great impact of the boundary and strong points in the POLSAR image, the following density becomes unstable. The saliancy image which is based on the information entropy is proposed to remove these points before classification. The feature in H//A/SPAN space of the remaining pixels is weighted with the saliancy value. Then the unsupervised classification is achieved based on the FDP. In the experiment with the ESAR data, results validate the effectiveness of the new method.