As a basic technique against the multi-baseline phase unwrapping problem, Chinese remainder theorem (CRT) restricts itself in the application of multi-baseline phase unwrapping due to its worse noise robustness. However, multi-baseline phase unwrapping algorithms based on the clusteranalysis are able to overcome the drawbacks of the traditional CRT method. Based on the cluster-analysis method in the CANOPUS algorithm, an L-∞-norm is employed to define the distance between two elements to decrease the production of very small clusters and the number of noise points so as to improve the cluster-analysis (CA) performance. Besides, the procedure of the CA method in the CANOPUS algorithm is changed to improve the efficiency of the method and decrease the consuming time of the algorithm. According to the experiments on a simulated and repeatpass real interferometric synthetic aperture radar (InSAR) dataset, the effectiveness and efficiency of the improved clusteranalysis method are tested.

Traditional polarimetric target decomposition(PTD)methods decompose the scattering mechanism of every pixel into a weighted sum of basic scattering mechanisms,in which the whole feature of assemblies can hardly be revealed.Moreover,the man-made targets are frequently of low signal-to-noise ratios.Directly applying the PTD methods can hardly reveal the real scattering mechanism of targets.Therefore,the fully polarimetric attributed scattering center model is brought in to decompose the target signal into a set of classical attributed scattering centers to preserve integral properties.Moreover,the parameter is estimated with a high anti-noise property by joint polarization.Based on these,a novel polarimetric target signal reconstruction method jointed with attributed scattering centers is proposed in this paper.The proposed method is capable not only of reconstructing a target signal under low signal-to-noise ratios but also of preserving the integral properties of typical attributed scattering centers.As a result,the visualization of the polarimetric image has been improved effectively.

Moving targets detection and imaging for multichannel terrain observation by progressive scans(TOPS)synthetic aperture radar ground moving targets indication(SAR-GMTI)has important application prospects.However,the azimuth bandwidth of the background clutter is much larger than the instantaneous signal bandwidth.Therefore,the background clutter will be highly aliased if the pulse repetition frequency(PRF)is limited.Direct application of space-time adaptive processing(STAP)techniques will significantly compromise the clutter suppression performance.Hence,we propose a spacetime adaptive processing method based on spectrum compression.The spectrum compression process can reduce the azimuth bandwidth of the background clutter to the instantaneous signal bandwidth.Space-time adaptive processing is then performed to effectively suppress the clutter,and the clutter suppression performance is greatly improved.Furthermore,Deramp processing is used to focus the moving target in order to avoid the target ambiguities arising from the Doppler shift.Meanwhile,we can perform the moving targets tracking by utilizing the highly overlapping full-aperture images which are obtained by the TOPS SAR mode.Simulation results validate the effectiveness of the proposed algorithm.

Based on the fact that anisotropy characteristic represents some intrinsic properties of scattering centers, an algorithm for extracting anisotropy characteristic of scattering centers is proposed when wide-angle SAR is used. Firstly, the components of the echo from a single scattering center are analyzed based on attributed scattering center model. Secondly, the estimation of anisotropy characteristic is transformed into an inverse issue of solving variation of single scattering centers amplitude by using identity matrix as orthonormal basis. Finally, the inverse issue is solved under the constraints that the amplitude of scattering centers should be real and continuous. As a result, anisotropy characteristic is extracted from the solution of the inverse issue. Estimation results of both Matlab simulated and electromagnetic computation data validate that the proposed algorithm is not only precise but also robust. Moreover, the proposed method is more efficient compared with traditional methods.

Due to its advantages of lightweight, cost effective and high resolution, the combination of frequency modulated continuous wave (FMCW) technology and circular scanning synthetic aperture radar (CSSAR) promise a bright future in the application of fast large area imaging. In order to handle the CSSAR signal and consider the residual secondary phase error introduced by FMCW technique, a new imaging algorithm suitable for FMCW-CSSAR is proposed, and precise imaging is realized with range highly approximated and residual secondary phase errors fully compensated. Simulation results of point targets are given to prove the effectiveness of the proposed approach.