Multichannel parameter estimation is a key technology for azimuth multichannel terrain observation by progressive scans synthetic aperture radar (MC TOPS SAR) systems. Normally, for the traditional multichannel parameter estimation algorithms, the mismatch parameters are concluded as phase error, baseline error, and Doppler centroid. The beam rotation speed error is ignored. However, for MC TOPS SAR, beam rotation speed error will degrade the reconstruction performance of the Doppler spectrum. Moreover, only part of the mismatch parameters can be estimated for the traditional algorithms. To address those problems, a joint parameters estimation algorithm for TOPS SAR based on spatial time cross correlation coefficient (STCCC) is proposed. Combined with phase wrapping and the least-squares method (LSM), the algorithm is capable of realizing a joint parameters estimation of channel consistency error, azimuth baseline error, Doppler centroid frequency, and beam rotation speed estimation without iteration. The real data processing result shows that the method can effectively realize MC TOPS SAR data error estimation.

Devolution technique and linear inversion can be utilized in forward-looking imaging radar. However, complicated forward-looking geometry with curve trajectory for high-speed dynamic platform makes the traditional model mismatched, and the conventional method inefficient. In this paper, a superresolution forward-looking imaging algorithm based on extended sparsity and step-size adaptive matching pursuit (ESSAMP) under the optimized Doppler convolution model is proposed. The main contributions are that the multi-domain cascade phase factor (MCPF) is constructed to compensate for unified range cell migration introduced by complicated forward-looking geometry and high-order phase introduced by three-dimensional (3-D) acceleration. Then, the optimized Doppler convolution model for forward-looking imaging is derived. The modified over-complete dictionary with Doppler phase information is then fully exploited to perform the Doppler deconvolution. Besides, the forward-looking imaging problem is solved by the proposed ESSAMP method. Numerical simulations validate that the proposed algorithm is effective to reconstruct forward-looking scenarios, robust to noise, and efficient to implement.

Synthetic aperture radar (SAR) sometimes needs to be imaged with small time bandwidth product (TBP) for fast observation. As a real-time image algorithm, back-projection algorithm (BPA) is efficient for fast observation. However, compared with the frequency-domain algorithm, the influence of small TBP (STBP) on BPA has not been fully discussed. Thus, the performance of the original BPA on STBP SAR imaging is analyzed and an improved BPA for STBP imaging is proposed in this letter. Theoretical analysis indicates that STBP will cause the point spread function (PSF) distortion and azimuth spectrum ambiguity to the BPA imaging result. To deal with those problems, two parameters of the original BPA are modified in the improved BPA. The accumulating azimuth angle (AAA) is optimized first to calibrate the PSF distortion. Then, based on the azimuth-ambiguity-to-signal ratio (AASR), a BPA meshing criterion related to TBP is proposed to diminish azimuth spectrum ambiguity. The simulation and real data results demonstrate that the improved BPA is effective on undistorted PSF recovering and unambiguity spectrum maintaining, even when the TBP is down to 4.

To obtain digital elevation model (DEM) data through spaceborne interferometric synthetic aperture radar (InSAR) technology, the solution of nonlinear InSAR geolocation equations is one of the most important steps. On one hand, Newton iterative method is time-consuming to solve the equations. On the other hand, the conventional closed-form solution for low earth orbit (LEO) bistatic InSAR system is not suitable for the novel InSAR configuration of geostationary (GEO) transmitter with LEO receivers because of the significant geometry difference. To address these issues, this letter analyzes the bistatic GEO-LEO geometry in detail and exploits the bistatic InSAR equations to propose a GEO-LEO fast location method (GFLM), which obtains the effective closed-form solution for the novel system. Compared with the general Newton iterative method, the GFLM significantly improves the efficiency of geolocation for the bistatic GEO-LEO InSAR system with high precision. Moreover, the geolocation accuracy of the novel system under realistic parameters uncertainties is introduced. Finally, we carry out simulation experiments to verify the effectiveness and superiority of GFLM.

In terrain observation by progressive scans’ synthetic aperture radar (TOPS SAR) imaging, 3-D acceleration of maneuvering platforms makes conventional unfolding methods no longer applicable. Moreover, when the burst is long enough, the approximation of frequency-domain algorithms exceeds the margin of error. To solve these problems, a modified extended range migration algorithm (ERMA) with generalized azimuth resampling is proposed in this letter. First, the regularized 2-D spectrum is reconstructed by an uniform acceleration compensation and unfolding operation. Subsequently, generalized resampling eliminates the spatially variant acceleration phase and equalizes the Doppler parameters of targets within the same range cell. Finally, the TOPS SAR data are focused in azimuth wavenumber domain. The proposed method is supported by point target simulation.

Combined with terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR), near-nadir SAR (N-SAR) system has great potential for the surface water and ocean topography (SWOT) observation. However, one practical problem of the airborne N-SAR is the non-ideal attitude angle caused by the environment. Normally, the influence of attitude angle error can be considered as a constant squint angle of the system. However, in fact, for the near-nadir SAR system with TOPS SAR mode, the attitude angle error will cause a non-ideal 2-dimensional space-variant Doppler centroid for the receiving data and cause the traditional imaging algorithm failure. To deal with such problems, an imaging algorithm with attitude angle estimation for the near-nadir TOPS SAR system is proposed in this paper. Firstly, the influence of attitude angle error and the signal properties are analyzed. Secondly, based on the analysis, an attitude angle estimate algorithm (AAE), utilizing the nonlinear least square method (NLSM) and minimum entropy criterion, is proposed. Then, based on the estimated altitude angle, without data blocking, a full data imaging algorithm (FDA) is proposed. Its main idea is to obtain an un-ambiguous azimuth spectrum and design an appropriate range-variant azimuth filter through frequency chirp scaling (FCS). The numerical simulations and real data processing demonstrate that the proposed algorithm has good performance on attitude angle error estimation and well-focused image obtaining.

Combined with Terrain Observation by Progressive Scans (TOPS) Synthetic Aperture Radar (SAR), Near-nadir Interferometry SAR (NInSAR) has great potential on the Surface Water and Ocean Topography (SWOT) observation. One practical problem is that the Doppler center of airborne NInSAR varies along range and its bandwidth is greater than pulse repeat frequency (PRF) due to the affection of yaw angle error. Traditional imaging algorithms divide the data into several range blocks so that the range variation of Doppler center is ignorable. However, the drawback is that the size and the overlap of the blocks are hard to design. In this paper, an improved image algorithm without range block is proposed. The main procedure is to obtain unambiguous azimuth spectrum and design appropriate range varied azimuth filter through frequency chirp scaling algorithm. The good performance of the proposed algorithm is demonstrated through point target simulation and real data.

Polarization Orientation Angle (POA) estimation and compensation with P-band, L-band and C-band PolSAR data are investigated previously. In this paper, we focus on the POA estimation performance with X-band PolSAR data. We analyze the effect of SAR image resolutions, signal to noise ratio (SNR) and scattering characteristics on POA estimation. Through the processing results of the airborne PolSAR data and TerraSAR-X PolSAR data, we can see that the POA estimation can be successfully executed in the building areas and non-vegetation covered areas, which will facilitate the PolSAR and PolInSAR processing. However, POA estimation will fail in other areas except for the built-up regions when the SAR image resolutions are below 3 meters even the high SNR of X-band PolSAR data. We illustrate the conclusions by PolSAR data experiments.

Vegetation canopy height inversion is a significant research content in Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) surface parameter inversion. One practical challenge is that the traditional three-stage inversion method will lead to the inaccurate inversion of vegetation canopy height in X-band because of the too concentrated distribution of polarization interference coherence coefficients (PolInCc). In this paper, an X-band PolInSAR vegetation canopy height inversion strategy is proposed based on Frequency Segmentation (FS). The FS divides the original PolInSAR images into multiple PolInSAR images called sub-images, and the sub-images are used to estimate the more near-linear PolInCc distribution of the sub-images by interferometry. Then the precise terrain phase and effective volume coherence coefficient can be obtained to invert the vegetation canopy height accurately. The validity of the proposed method is verified by N-SAR X-band real data.

This letter presents a new approach of digital elevation model generation using circular synthetic aperture radar. The approach is composed of two steps, target separation and height estimation. In step 1, subaperture images are reshaped into vectors and stacked to build a composite matrix. The composite matrix is decomposed into a low-rank matrix and a sparse matrix based on “Semi-Soft Go Decomposition” algorithm. The targets, whose height is equal to the reference imaging height plane, are contained in the low-rank matrix, whereas the targets with other heights are contained in the sparse matrix. In step 2, the sparse matrix is further decomposed into several shifted low-rank matrices, each of which corresponds to the targets sharing one height, based on “Shifted Subspaces Tracking” algorithm. The height of the targets contained in each low-rank matrix is estimated from the shifts of the matrix's subspaces. The effectiveness of the proposed approach is investigated by the Gotcha public release data set.

Spaceborne bistatic synthetic aperture radar (SAR) system with an inclined geosynchronous (GEO) illuminator and a low-earth-orbit (LEO) receiver is capable of providing a vast area of surveillance and fine spatial resolution, which presents huge potentials for future earth observation. In this letter, inclined GEO-LEO azimuth multichannel SAR (MC-SAR) system for high-resolution wide-swath imaging is investigated. Starting from modeling geometry of inclined GEO-LEO bistatic MC-SAR, the signal model is analyzed in detail for the first time, and the equivalent positions of the received channels are obtained. Then, we found the spatial-variant residual phase error cannot be compensated accurately by conventional effective phase center (EPC) processing. Moreover, because of the complex bistatic configuration, the frequency-domain imaging algorithms become extremely difficult to achieve a well-focused and phase-preserved image. Meanwhile, the time-domain back-projection algorithm (BPA) faces with a technical challenge due to the nonuniform sampling in azimuth. To address these issues, a bistatic weighted BPA (BWBPA) for GEO-LEO MC-SAR is derived and presented. Without the procedure of EPC, the BWBPA can suppress azimuth ambiguities effectively and yield phase-preserved SAR images. Simulated data results show the validity of the presented method.

This study considers the phase noise filtering problem for interferometric phase image using sparse optimisation technique. Since the original model can be formulated as a rank minimisation problem, it is difficult to solve. One appealing approach is to use a nuclear norm (NN) regularisation to relax the rank regulariser. However, the performance of such approach is not satisfying. In this study, the authors propose to use reweighted NN regularisation to approximate the rank regulariser, which leads to the low‐rank reformulation. Though this reformulation is non‐convex, a new algorithm termed as spatially adaptive iterative weighted singular‐value thresholding algorithm is proposed to effectively solve it. Specifically, the weight and image variables are updated alternatively by block coordinate descent iteration scheme. In addition, the corresponding computational complexity of the algorithm has been established. Simulation results based on simulated and measured data show that this new phase noise reduction method has much better performance than several existing phase filtering methods.

The Goldstein filter is the commonly used filter for interferometric synthetic aperture radar (InSAR) phase noise reduction, and its performance is determined by three parameters: the power spectrum filtering parameter α, the patch size of the InSAR phase, and the smoothing window on the power spectrum. However, the noise level of the different regions in an interferogram changes greatly due to the terrain topography. The InSAR phase will be overfiltered in high-coherence regions and be underfiltered with high noise level in low-coherence regions if the unchangeable processing parameters are used. In this paper, we propose an improved InSAR phase filter in frequency domain. First, we use different window sizes to suppress the phase noise, and the window size is determined by the coherence of the central processing pixel. A pixel with higher coherence uses a smaller filtering window size and vice versa. Second, the samples in the filtering window are not independent and identically distributed samples for the terrain topography. To deal with this problem, we use fringe frequency compensation to eliminate the effect of the terrain topography. Third, combining with a newly defined filtering parameter α, we execute the phase filtering procedure pixel by pixel, which is different from the Goldstein filters. Furthermore, we use the Fourier transformation of a small window (generally 3 × 3) to deal with the power spectrum of the InSAR phase patches. Compared with other improved Goldstein filters and spatial-based filters, the effectiveness of the newly improved InSAR phase noise reduction method in frequency domain is investigated by both simulated and real data sets.

Interferometric synthetic aperture radar (SAR) filtering is a crucial processing step in topography reconstruction and deformation measurement. The high-resolution interferograms over vegetation or urban areas are heterogeneous, which will violate the local stationarity assumption and make it difficult to obtain a large number of independent and identically distributed samples for interferometric noise suppression. To overcome this problem, a refined nonlocal filter is proposed in this letter. Given coherence, topography phase, and expected SAR intensity of the pixel to be filtered, the similarity distance between the central pixel and other pixels in the searching window is measured by the normalized probability density function of the interferogram. Then, the outliers whose normalized probability density is smaller than the given threshold are removed from the filtering process. Finally, both simulated and real high-resolution interferograms are used to evaluate the effectiveness of the proposed method.

In this paper, a comparison between four channel error estimation methods for high resolution and wide swath (HRWS) synthetic aperture radar (SAR) systems is present. Three of the methods are based on subspace theory and implemented in Doppler frequency domain, while the fourth is based on the correlation between adjacent samples and implemented in time domain. After a brief overview of the approaches, the performance of each is analyzed with respect to its computational complexity and precondition. Quantitative results are shown using the ground-based real-data.

Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space-time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.

A new approach is presented to estimate the fringe frequencies in interferometric synthetic aperture radar (InSAR) phase image. A first-order model is usually used for fringe frequency estimation, but in steep regions or low-correlation regions, it often fails. In this letter, a prefiltered interferogram, obtained by the slope-compensated or conventional mean filter, is divided into small patches and unwrapped separately. Subsequently, we differentiate the local-phase-unwrapping results to obtain the fringe frequencies. Furthermore, the invalid fringe frequencies are eliminated by a statistical threshold. Finally, the interferogram is filtered by compensating the estimated fringe frequencies in the averaging window of the mean filter. The proposed method can obtain continuous fringe frequency estimation, and it is not constrained by the first-order model. The effectiveness of the proposed approach is verified by the simulated and real InSAR data.

As is well known, image coregistration, interferometric phase noise suppression, and phase unwrapping are three key processing procedures of synthetic aperture radar interferometry (InSAR). The three procedures are cascaded in the conventional processing flow of InSAR. Unlike the conventional processing flow, in this paper we propose a joint processing idea to carry out image coregistration, interferometric phase noise filtering, and phase unwrapping simultaneously based on subspace projection for multibaseline InSAR systems. The joint processing method can perform the fine coregistration of all SAR images implicitly by extracting the correlation information in the neighboring pixel sets, suppress the phase noise by utilizing the orthogonality of the signal subspace and the corresponding noise subspace, and optimally estimate the unwrapped interferometric phases (or the terrain heights) by combining the pixel coherence and the baseline diversity of a multibaseline InSAR system. Simulated results are presented to verify the effectiveness of the joint processing method