Synthetic aperture radar tomography (TomoSAR) is widely used for 3-D imaging in urban environments. Reference network (RN) technology has emerged as an effective solution in TomoSAR, leveraging a spatially correlated network of persistent scatterers (PSs) to mitigate the atmospheric phase screen (APS), a significant source of error in multipass spaceborne SAR systems. An effective RN should connect sufficient PSs with reliable edges, thereby ensuring comprehensive tomographic imaging of PSs and other scatterers around PSs. However, RN construction faces challenge in simultaneously addressing both network connectivity and quality during RN construction. To resolve this issue, this article introduces an innovative approach for constructing an optimal RN based on the minimum spanning tree (MST-RN). In this approach, the paradigm for determining the optimal RN is transformed into the problem of finding the MST in which the relative elevation estimation quality of the edge in RN is regarded as their weight in MST. Furthermore, standardized metrics are proposed to comprehensively evaluate the effectiveness of RN. The proposed MST-RN method demonstrates superior effectiveness on the metrics including overall connectivity quality, PS candidate (PSC) coverage, and network redundancy, compared to existing methodologies, which underscores the significant progress made in the RN construction. Finally, the effectiveness of the proposed method is validated through experiments conducted on the TerraSAR-X dataset acquired in Shenzhen, China, demonstrating accurate height estimation of PSs in urban areas.

Based on the multi-view airborne SAR location model, this paper deduces the transfer model of the influence which is about aircraft motion error on the target location error. The model is applied to the target location error analysis of traditional multi-view airborne SAR system, and the mechanism of multi-view airborne SAR location model can improve the target location accuracy is revealed. Compared with using signal simulation experiment which need the iterative solution of the location equations to analysis the location error, the model is simple and efficient, and it's location error analysis accuracy can reach millimeter-level. It can provide theoretical support for the analysis of factors affecting location error and the error correction of multi-view airborne SAR.

This letter introduces a multiple-scatterer detection method in synthetic aperture radar tomography (TomoSAR), named iterative sequential generalized likelihood ratio test (iterative-SGLRT). In this technique, the number of scatterers is sequentially decided by the generalized likelihood ratio test (GLRT) pixel by pixel, after iteratively estimating the parameters. It is a good tradeoff of the aforeproposed methods of sup-GLRT and fast-sup-GLRT on accuracy and efficiency. Simulated comparisons showed that iterative-SGLRT outperformed fast-sup-GLRT on the performances of detection probability and accuracy without substantial computation time increase, and compared with sup-GLRT, its performance loss could be negligible with computational burden greatly reduced. In addition, both iterative-SGLRT and sup-GLRT have been applied to the TerraSAR-X data set over Shenzhen city. The 3-D reconstruction of the test site and the separation of the overlaid scatterers have been achieved. In addition, verification using light detection and radar (LiDAR) indicated a root-mean-square error (RMSE) of $ {0.1\rho _{s}}$ for both methods of the height estimated. Accordingly, iterative-SGLRT is very suitable for large urban area processing for its super-resolution, high efficiency, and robustness.

As a typical propagation error, tropospheric delay has been fully considered during synthetic aperture radar (SAR) interferometry and differential interferometry, while the influence on the SAR imaging process is just concerned in advanced configurations, e.g., staring mode, high-resolution, geosynchronous/geostationary (GEO) SAR. In this article, a comprehensive model of turbulent tropospheric delay (TTD), involving atmospheric sciences and fluid mechanics, is introduced, and the influence of which on high-resolution SAR is analyzed. In order to compensate for the influence of TTD in advanced SAR missions, the autofocus method used in motion error compensation is extended, and a 2-D phase error estimation and compensation algorithm is proposed. Subsequently, the simulation experiments of both dot-matrix targets and TerraSAR-X data are performed to validate the effectiveness of the proposed algorithm. Conclusions and prospects are reached in the end.

This article presents an efficient 2-D baseband azimuth scaling (2-D BAS) approach for the focusing of data acquired in the spaceborne squinted sliding spotlight and high-resolution wide-swath (HRWS) terrain observation by progressive scan (TOPS) imaging modes. The existing approaches can become inefficient or invalid due to the coexistence of central “absolute” and marginal “relative” squint in the above-mentioned modes. In this article, the signal properties of spaceborne squint synthetic aperture radar (SAR) with the antenna electronically steering in the azimuth dimension are analyzed first, based on which a new parity-decomposition-based range equation (PDRE) is presented to dedicatedly model the range histories of targets illuminated by the rotating beam. A novel 2-D BAS approach is then developed to remove the odd asymmetric part but preserve the even symmetric part of PDRE, which means a “true derotation” for eliminating the effect of both absolute and relative squints caused by the beam rotation. An even-order-multinomial perturbation function is applied and integrated into a range-Doppler-based SAR processing kernel to efficiently compensate the azimuth variation of high-order range cell migration (RCM) and azimuth phase modulation caused by the 2-D BAS. The proposed processor is efficient, since none of the data extension, the postprocessing for resolving focused-domain folding, or the 2-D frequency-domain interpolation at high squint is needed. Simulations with point targets in the squinted sliding spotlight and HRWS TOPS modes are used to validate the developed algorithm.

VideoSAR (video synthetic aperture radar) can provide continuous surveillance over a specific region, and shadow can be utilized to detect moving target. However, feature variations (unstable scattering properties and continuous motion) may seriously damage the precision of registration. Speeded Up Robust Feature (SURF) is one of the most popular method for feature detection and SAR image registration. So in this paper, a robust image sequence registration algorithm for videoSAR is proposed based on the combination of SURF and inter-frame processing. SURF is firstly introduced to videoSAR registration, whereas the continuous feature variation through the entire image sequence, showing a severe loss of robustness . Aiming at this defect, SURF and the idea of inter-frame processing are integrated and forming a novel algorithm. Simulation experiments underline the effectiveness and robustness of the proposed method in videoSAR image sequence registration.

Synthetic aperture radar (SAR) is an advanced imaging sensor that can image side-looking terrain. Until now, SAR schemes and imaging theories have been researched for stationary scenes. Unlike a stationary scene, due to unknown motion parameters, a ground moving target (GMT) is shifted and defocused in a background image, which causes difficulties in their use for further applications. This paper proposes a multiple pulse repetition frequency (PRF) airborne SAR scheme for background-free GMT imaging. In the proposed scheme, pulses are transmitted by a nonuniform pulse repetition time, and echoes are divided into groups with different PRFs. For spectra with different PRFs, due to their diverse respective spectra extending periods, periodical extending GMT spectra will not appear at the same location except for the original spectra. A theorem is proposed to prove that the spectra can be extracted intact, undistorted, and unpolluted. In light of the divergence of the GMT spectra location from that of the clutter, filter banks are designed to locate, extract, and reconstruct the GMT spectra out of the mixed spectra that contain the GMT and clutter. For GMT SAR imaging, a moving target is regarded as a steady target under an equivalent geometry with a new equivalent squint angle and a new SAR platform velocity. The SAR image is obtained with the range-Doppler algorithm with equivalent geometry parameters that are estimated from the Doppler parameters. Experimental results obtained from using extensive numerical simulated data validate our proposed approach.

Imaging of a ground moving target (GMT) with synthetic aperture radar (SAR) is a challenging task, particularly when the Doppler ambiguity happens. This paper proposes a novel GMT range model and a novel Doppler ambiguity-tolerated GMT imaging algorithm based on modified keystone transform. In the proposed GMT range model, the range from radar antenna to the scattering center on the target is divided into two parts: the range from radar antenna to the target centroid and the projection length on the light of sight from the target centroid to the scattering center. Based on the new range model, a Doppler ambiguity-tolerated range cell migration correction (RCMC) method is proposed, in which keystone transform is modified to realize the differential RCMC by interpolation with nonzero phase sinc kernel. Finally, the GMT image is obtained in the range-Doppler domain by matched filtering in the slow-time domain and is restored into the 2-D space domain. The effectiveness of our proposed model and imaging method is demonstrated by both simulated and real airborne SAR data.

At L-band, the ionosphere is assumed to have considerable effects on differential Synthetic Aperture Radar Interferometry (D-InSAR) image quality [1]. This paper focuses on ionospheric propagation effects from difference of slant range total electron content (ΔSTEC) and error calibration for D-InSAR. Referring to International Reference Ionosphere (IRI) model, we simulate the distribution of ionosphere slant range total electron content (STEC) in Hainan. Then the phase error and deformation error from ionospheric effects are analyzed. The performance of two ionospheric correction methods is also given. The `group-phase delay difference method' [2] is expected to have a lower precision than the `spectrum-split method' [3] in the limit of regular image registration accuracy. Some suggestions about the smoothing window of the latter are given. Finally, with updated SBRAS simulator, an L-band D-InSAR simulation experiment is conducted and the `spectrum-split method' is validated. Experiment results show that about 0.02TECU ΔSTEC and less than 1cm residual deformation error from ionospheric effects are available for L-band D-InSAR.

A multi-mode space-borne Synthetic Aperture Radar (SAR) simulator is presented in this paper. The simulator is based on the Space-borne Radar Advance Simulator (SBRAS), and supports the parameter design, raw data simulation, data focusing and performance evaluation of strip-map mode, spotlight mode, sliding spotlight mode, ScanSAR mode, Terrain Observation by Progressive Scans (TOPS) mode and mosaic mode. The hardware architecture and software composition are described below. At last, an application example of high resolution sliding spotlight with point targets and another application example of wide swath TOPS mode with distributed targets are provided.

Recently, a synthetic aperture radar (SAR) tomograhic focusing method based on compressive sensing was proposed. This focusing method can reduce the required number of measurements and achieve satisfying elevation resolving ability. First, we briefly review this novel focusing method and prove the applicability of compressed sensing (CS) for SAR tomography theoretically using the latest improvement of CS. Then, we apply this focusing method to the 3-D reconstruction of Angel Stadium with Envisat-ASAR data. Both the theoretical analysis and satisfying results of real data processing confirmed the applicability of this SAR tomograhic focusing method.

This letter presents a new efficient phase filtering algorithm for synthetic aperture radar interferometric phase images. Based on the additive noise model, we assume that the phase without noise is composed of principal phase components and residual phase components. A new two-step adaptive filtering algorithm based on this assumption is then developed. First, the principal phase components are adaptively estimated using the frequency spectrum with an adaptive bound and removed from the original noisy phase; thus, a residual noisy phase is obtained. Second, spatial filtering is carried out on this residual noisy phase image using four directional masks. The filter equals three other common filters when three different special bound parameters are set accordingly. This algorithm is effective, particularly for the interferometric phase images with tightly packed fringes or low coherence. Numerical results obtained with synthetic and real data show a significant improvement with respect to other conventional filtering algorithms in some situations of the proposed approach.

Two approximations are used in "Three-pass" D-InSAR (Differential SAR Interferometry). First, in this paper, the error caused by the approximations is deduced and defined as First-order Approximation Error. Then, the characteristics of First-order Approximation Error are discussed in theory and proved by two experiments. In the end, three viable and effective methods are put forward to correct approximation error.

According to fractal ocean surface model, electromagnetic scattering model under Kirchhoff approximation and the raw signal simulation procedure of dynamic scene based on time domain, spaceborne synthetic aperture radar (SAR) raw signal of ocean scene is generated. The SAR images obtained from the echo of both simple cosine wave and complex fractal ocean surface are in accordance with the tilt modulation and the velocity bunching theoretically, and also with the statistical properties of real ocean SAR images, which validate the simulation procedure. The raw signal could be the input data of studying along-track InSAR (ATI) for ocean current measurements.

Differential Inerferometry (D-InSAR) for spaceborne bistatic SAR is studied. According to advantages of the system, particularities and superiorities of D-InSAR based on spaceborne bistatic SAR are discussed first. Then, solid baseline is decomposed and the model of “three-pass” D-InSAR is established. Afterwards, the theory of data fusion is introduced to D-InSAR and a new method is put forward to avoid the max detection error. A simulation experiment accompanied by very good result is shown in the end.