The least-squares (LS) method is commonly employed to retrieve the 3-D wind field by joint detection with multiple sensors, but it tends to suffer from ill-conditioning and reduced accuracy when the sensors are closely deployed and heterogeneous. This letter proposes a new method, denoted as TSVD-LSBE to address the ill-conditioning and sensor heterogeneity problem. First, the LS algorithm, regulated by the truncated singular value decomposition (TSVD), is utilized to get a preliminary estimation of the wind field. Then, the modified Bayesian estimation (BE), which is also regulated by TSVD and uses the modified LS result as the background wind, is employed to tune the preliminary estimation to a more accurate one. Simulation results demonstrate that the new method can enhance the retrieval accuracy by up to 50% in various wind field detection scenarios, surpassing both the modified-LS method and the local and global variational method (LGVM) based on single sensor measurement.

Over-the-horizon radar (OTHR) utilizes the ionospheric refraction and reflection in high-frequency band for air-sea targets detection. However, non-stationary ionospheric dynamics induce inhomogeneous distortions in sea clutter and targets, significantly degrading detection and localization performance. To address this challenge, we present a comprehensive investigation on OTHR full-link modeling and simulation to systematically analyze the impact of various trans-ionospheric propagation effects on echo signals. In Part I, we develop a unified framework for full-link modeling of sea clutter that incorporate background ionospheric and oceanic conditions, enabling simulation and analysis of sea clutter characteristics. Firstly, we establish the models for radar cross section of sea clutter and ionospheric propagation effects. Key parameters, including the wave propagation range, actual range, group delay, phase disturbance, and propagation loss, are calculated based on the Appleton-Hartree formula and ray tracing technique. Secondly, we construct the echo signal models in the fast- and slow-time domain and derive the corresponding range-Doppler spectrum. The origin mechanism and intrinsic cause of Doppler shifting, broadening, and splitting, as well as range localization errors are theoretically analyzed. Finally, simulation experiments of three scenarios are designed to produce OTHR sea clutter data in sea and air modes, which are validated in comparison with the real data. The typical phenomena of Doppler shifting, broadening, and splitting observed in real data are reproduced, and the results indicate that the multi-mode propagation is the main obstacle to range localization and ionospheric decontamination. The full-link sea clutter model provides critical insights for the subsequent signal processing tasks including ionospheric decontamination, clutter suppression, target detection, and localization.

The phase difference (PD) algorithm offers effective support for fine imaging in the airborne synthetic aperture radar (SAR). However, for the stripmap mode, the incomplete phase history may reduce the robustness of PD. Considering it, two improvements regarding weighting and positioning in PD were proposed. The signal-to-clutter ratio (SCR) weighting can imply the phase history incompleteness and the peak positioning via phase information can achieve higher estimation accuracy than the upsampling method. Combining them, the stripmap phase difference (SPD) framework was given. Simulation and real data results validated its effectiveness.

Due to Earth’s rotation and the elliptical orbit of GEO SAR, the relative velocity between the ground target and the satellite is not perpendicular to the beam center, resulting in the Doppler centroid being nonzero. It not only brings great challenges to imaging but also reduces interference accuracy and ground-moving target indication (GMTI) ability. This letter proposes an accurate 2-D pitch–roll attitude steering method for geosynchronous synthetic aperture radar (GEO SAR) antenna, which can achieve zero Doppler centroid and greatly reduce the maneuvering angle of satellite antenna on the premise of ensuring that the Earth’s surface can be illuminated by the beam. The angle variation is basically less than 0.22°, which is much smaller than other methods. The proposed attitude steering method can realize the real-time irradiation of GEO SAR to the Earth’s surface and can greatly reduce the difficulty of GEO SAR antenna engineering implementation compared with the traditional attitude steering method. The effectiveness of the proposed method is verified by simulation experiments.

Geosynchronous synthetic aperture radar (GEO SAR) operating in the squint mode has the advantages of wide coverage and short revisit, which can improve the ability of emergency response and continuous observation compared with broadside mode. Due to the sensitivity of GEO SAR to the squint angle, a large amount of data redundancy and severe 2-D spatial variation problems are encountered in imaging processing. By utilizing the variable pulse repetition interval (VPRI) technique, the data reception window is adapted to accommodate changes in range cell walk, thereby eliminating redundant data. The current frequency-domain imaging algorithm is not suitable for nonuniformly sampled data. This article proposes a method based on nonuniform fast Fourier transform (NUFFT) and modified range-Doppler azimuth chirp scaling (RD-ACS) algorithm for processing squint GEO SAR data. Azimuth NUFFT can obtain data with equidistant Doppler frequencies. A 2-D frequency spectrum of reference points within the scene is used for bulk phase compensation. The slant range is represented by a high-order polynomial containing 2-D spatial variations. During range cell migration (RCM) correction and azimuth compression process, the varying coefficients of slant range with range are used to replace the corresponding reference point coefficients for range space-variant compensation. The azimuth spatial variation is addressed by the CS method, and the phase introduced by the scaling equation is compensated and the azimuth compression is completed in the RD domain. The computational complexity of the algorithm is discussed. Finally, the correctness of the proposed algorithm is verified by simulation experiments.

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.

Geosynchronous Synthetic Aperture Radar (GEO SAR) has important potential applications in the fields of atmospheric water cycle and digital weather forecast, and establishing the connection between GEO SAR imaging and atmospheric water vapor inversion is a key step. The turbulent variation of atmospheric phase delay will cause the SAR image defocus, and turbulent tropospheric delay phase estimation based on block autofocus method in low-orbit high resolution space-borne SAR can be used for reference, but this method is very sensitive to the data block selection, modulation frequency (FM) outlier detection and correction. This Letter gives some considerations to these two problems of block selection and outlier correction. Based on the idea of Extreme Learning Machine (ELM), a mathematical model with nonlinearity as the core and linear model as easy to solve is established. When the parameters affecting the turbulent troposphere are known, the model can be directly used to obtain the block optimization selection, namely, the number and size of blocks. When the parameters are unknown, the model can be used to roughly inverse the parameters and provide initial values for iteration. For the second problem, using the prior information of the correlation of the FM estimated by the adjacent sub-blocks, we proposes a Local Outlier Factor (LOF) method to identify the error point by defining the distance threshold. The effectiveness of the proposed algorithm is verified by Sentinel-1 data injected with tropospheric error.

This letter introduces a reliable three-dimensional reconstruction for buildings using a small data stack in synthetic aperture radar tomography (TomoSAR) based on a revised Reference Network (RN) method. The RN method can realize atmospheric phase error correction by persistent scatterer (PS) phase differencing. However, when dealing with a limited number of images in a small data stack, the inaccurate inversion of relative elevation for local PS arcs can result in error propagation during network integration, leading to compromised tomography results. To address this issue, we have refined the traditional RN method by introducing a new geometric constraint between PSs. In the proposed method, we utilize the relative elevation estimated from PS arc differential signal to accurately determine the spatial distance between PSs. PS arcs that exceed the geometric constraint are eliminated to assist in adjusting the reference network. The effectiveness of this optimization method is demonstrated through TomoSAR experiments conducted on a high-resolution TerraSAR-X SAR data stack in Shenzhen, China. Consequently, the method presented in this letter is highly suitable for processing small data stacks compared to the previous RN method.

The adjacent cross correlation function (ACCF) is an effective tool to remove the range migration (RM) and reduce the order of Doppler frequency migration (DFM) in the pulsed-radar echoes of maneuvering targets, and thus has been employed in radar target detection and imaging. However, the ACCF is a nonlinear transform, which means that the signal-to-noise ratio (SNR) variation will be introduced by this operation. This letter presents a quantitative analysis of SNR for the ACCF. The frequency-domain derivation and analyses of ACCF are presented. Based on this, the SNR variation of the ACCF is analytically derived. The theoretical derivations and analyses are confirmed by numerical examples.

The amplitude stripes induced by the ionospheric scintillation have been frequently observed in spaceborne L-band synthetic aperture radar (SAR) images, such as ALOS PALSAR, which may impede SAR applications. From another perspective, the presence of ionospheric stripes implies a reverse guidance for ionospheric sounding. In this paper, a methodology for correcting and measuring ionospheric stripes from SAR images is proposed. Firstly, based on prior information of the stripes’ orientation, the stripe components are automatically detected and extracted using a band-rejection spectral filter. Secondly, the extracted amplitude errors are further applied to measure ionospheric scintillation parameters by fitting the power spectrum. The methodology is tested on several PALSAR acquisitions. The results indicate that the methodology can automatically corrects the stripes and has a great potential of measuring ionospheric scintillation from the SAR images with amplitude stripes.

The phase scintillation induced by ionospheric irregularities can reduce azimuth decorrelation and cause defocusing in the low-frequency spaceborne synthetic aperture radar (SAR) imaging. Thus, it demands for an autofocus approach to correct the scintillation phase error (SPE) and to refocus the deteriorated SAR image. However, the azimuth variation of the SPE has been rarely considered and poorly tackled in previous research studies. In this letter, an ionospheric phase screen (PS) projection method of phase gradient autofocus (PSP-PGA) is proposed to deal with this issue and compensate the azimuth-varying SPE in SAR images. The PSP is used to project the SAR image onto the PS, by using the part decompression and spectral analysis, so as to realize the decoupling of the SAR data with SPE. Then, the projected image can be directly applied to the PGA to accurately estimate the SPE. The proposed PSP-PGA is validated by using several simulation processing experiments and its performance is finally evaluated with respect to the parameter of the PS height.

Spaceborne polarimetric synthetic aperture radar (Pol-SAR) systems operating at low frequencies, such as P-band, are significantly influenced by ionospheric Faraday rotation (FR). A novel theoretical model of the effect of FR dispersion on Pol-SAR images is proposed. Three components of the range compression response are derived with the main function accompanied by two accessories. Simulation results indicate that two accessory functions in existence of FR dispersion can be comparable with the main for P-band ultrawideband systems. It can not only lead to range imaging deteriorations but also bring about extra polarimetric distortions. Finally, an airborne P-band Pol-SAR real scene is used for validation.

The spaceborne low-frequency (L-band and below) synthetic aperture radar (SAR) is very susceptible to ionospheric scintillation. The scintillation phase error (SPE) is a vital factor that brings about the azimuth decorrelation and leads to the imaging degradation. In this article, a methodology is described, which exploits the accurate SPE estimation to measure scintillation parameters from SAR images. First, the phase gradient autofocus (PGA) is applied to achieve the believable SPE estimate from the local area that contains a strong scatterer. Second, based on the estimated staggered index of two SPE estimates, the irregularity altitude can be derived by solving an established equation using an iteration process. Third, three parameters, including the spectrum index, the outer scale, and the integrated turbulence strength, can be fitted when the theoretical spectrum expression mostly approximates the derived spectrum. The methodology is validated on an ALOS-2 PALSAR-2 spotlight data, and its azimuth imaging was seriously degraded by ionospheric scintillation. Two existent corner reflectors ensure the accuracy of SPE estimates from PGA. The measurement results indicate that the local ionospheric irregularities are extremely turbulent and located at a lower altitude of about 220 km in the F2-layer.

Leaf base and inclination angles are two critical 3-D structural parameters in agronomy and remote sensing for breeding and modeling. Terrestrial laser scanning (TLS) has been proven to be a promising tool to quantify leaf base and inclination angles. However, previous TLS studies often focused on leaf base and inclination angles of certain trees or plants with flat leaves, such as European beech. Few studies have worked on leaf base and inclination angles of maize plants due to their curved and elongated characteristics. In this study, a machine learning-based [support vector machine (SVM)] method and a structure-based [skeleton extraction (SE)] method were presented to extract the leaf base and inclination angles of maize plants. After separating individual leaf points from the complete point cloud and skeleton points of maize plants and then extracting geometric features, the machine learning- and structure-based methods were used to calculate leaf base and inclination angles. Our results show that the leaf base and inclination angles extracted using these two methods agreed well with ground truth, and the estimation accuracy of the machine learning-based method was obviously higher than that of the structure-based method. The mean absolute error (MAE), root-mean-squared error (RMSE), and relative RMSE (rRMSE) of the leaf base and inclination angles using the machine learning-based method were 4.56°, 6.17°, and 19.04% and 7.95°, 10.00°, and 20.24%, respectively; and those from the structure-based method were 6.22°, 7.47°, and 23.30% and 8.99°, 12.57°, and 25.85%, respectively. The machine learning-based method was also applied to a field with dense mature maize, and their MAE, RMSE, and rRMSE were 6.04°, 8.12°, and 25.90% and 11.30°, 13.52°, and 26.5%, respectively. It is demonstrated that both the machine learning- and structure-based methods are effective to estimate the leaf base and inclination angles of maize plants, although the machine learning-based method appears to outperform the structure-based method.

Geosynchronous (GEO) orbit and low-earth-orbit (LEO) in imaging have many differences, especially for relief of undulating three-dimensional(3D) terrain, the imaging errors caused by target elevation in two orbits of different altitudes are also diverse. The error caused by terrain elevation under the LEO SAR is negligible, and in GEO SAR the error will cause serious azimuth defocusing. This paper focuses on the error caused by topography elevation in LEO and GEO SAR respectively. Firstly, the SAR imaging geometric model including terrain elevation is established. Then, the source of error caused by terrain elevation and its influencing factors are analyzed. Finally, the error changes of the two orbits are analyzed, and the correctness of the proposed viewpoint is verified by experiments.

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 letter proposes a method for eliminating the paired-echo-like spurious peaks in sliding spotlight synthetic aperture radar (SAR) images caused by the electronic step-by-step azimuth beam steering. The basic idea of the method originates from the extended ideal filtering for suppressing the SAR azimuth ambiguities. The concept of deconvolving filtering is further extended and a new form of deconvolving filter is presented. Using the new filtering, not only the first and odd spurious peaks of point-like targets but also the even spurious peaks can be removed all at once from the detected SAR images. Simulated data from a sliding-spotlight SAR system with quantized azimuth steering are used to verify the improvement in image quality by using the new method.

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.

An L-band geosynchronous synthetic aperture radar (GEO SAR) system has to be confronted by an intractable issue of the decorrelations imposed by ionospheric irregularities. On the one hand, the phase and amplitude scintillations will bring about the decorrelation within the synthetic aperture and result in azimuth-imaging degradation. On the other hand, the imposed scintillation history is spatially decorrelated across the ultra-large GEO SAR scene. In this article, a signal model of the GEO SAR acquisitionis established with the two-way ionospheric transfer function (ITF) modulation to incorporate these two types of decorrelations. This model meanwhile takes the anisotropic and flowing irregularities into account. By using this model, the L-band GEO SAR azimuth-imaging is evaluated in terms of five indexes, whose performances are dependent on nine ionospheric parameters. Furthermore, the spatial correlation of the phase and intensity scintillation histories is investigated for the L-band GEO SAR scene, both in simulation and statistics. The statistical result implies a sized scene, in which the phase scintillation history tends to be consistent. Finally, the interferometric performance is investigated between the pure and contaminated GEO SAR images. The simulation result shows that the degradation of the interferometric coherence results from the in-aperture decorrelation.

There has been a burgeoning prospect in developing a spaceborne P-band synthetic aperture radar (SAR) mission for its stronger penetrability through foliage and subsurface than the higher-frequency system. However, the transionospheric signals operating at P-band are more susceptible to scintillation impacts, which may bring about the decorrelation of the signal amplitude, phase, and frequency introduced by ionospheric irregularities. In this article, a comprehensive numerical model of the generalized ambiguity function (GAF) is established to evaluate SAR imaging deterioration. On the one hand, an improved two-frequency and two-position coherence function (TFTPCF) is integrated in the GAF to include the amplitude scintillation derived from the diffraction. On the other hand, the anisotropic characteristics of the irregular structure is introduced into TFTPCF by adopting the Rino's 2-D spectrum. Furthermore, the ambiguous resolution is redefined for a more strict numeration. Numerical analyses about the ionospheric coherence and an ambiguous resolution are performed to investigate their sensitivity to scintillation parameters. Results show that this model is capable of depicting more comprehensive effects of the anisotropic irregular ionosphere, which may exhibit a rod-like structure, including elongation by anisotropic scale, rotation by geomagnetic heading, and projection by geomagnetic inclination. At last, the signal-level simulations are operated to verify the effectiveness of numerical conclusions, which further confirm that the structural configuration of anisotropic irregularities has a complicated effect on spaceborne P-band SAR image resolution.

The lack of high-resolution digital elevation model (DEM) data presents one major limitation for deformation mapping using synthetic aperture radar interferometry (InSAR) techniques with high-spatial-resolution radar imagery (e.g., TerraSAR-X). This article presents a baseline linear combination (BLC) approach to generate interferograms with nearly zero baselines so as to minimize the effects of the uncertainties in the DEM used. It incorporates the baseline combination (BC) method with adjacent gradient networking to successfully unwrap the interferograms even in abruptly discontinuous areas, which in turn can be used to estimate a high-resolution DEM. The BLC approach does not require any deformation model; instead, it utilizes nearly zero-baseline interferograms to assist with 3-D phase unwrapping. Application of the BLC approach to the TerraSAR-X data set in Shenzhen, China, shows that the BLC-derived DEM agrees with the digital surface model (DSM) obtained from light detection and ranging (LiDAR) with a correlation coefficient of 0.998 and a root-mean-square error (RMSE) of 2.05 m, demonstrating the effectiveness of the BLC approach. Note that the BLC approach is not only able to be employed in urban areas with high buildings but also in mountain areas with steep slopes.

In this paper, the anisotropic generality of the ionospheric irregularities is incorporated in the generalized ambiguity function (GAF) to evaluate its impact on spaceborne P-band synthetic aperture radar (SAR) imaging. The configuration of the anisotropic ionosphere exhibits as a rod-like structure, which is elongated by anisotropic scale, rotated by magnetic heading and projected by geomagnetic inclination. Aiming at these three parameters, numerical analysis is implemented in terms of the coherence and ambiguous resolution. At last, signal-level simulation is carried out to validate the effectiveness of the numerical results.

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.

As was indicated in the mentioned paper, the ionospheric stripes, in general, aligned well with the orientation of the projected ambient geomagnetic field vector. However, it is shown in our study that the calculated striping heading is not only dependent upon the orientation of the projected ambient geomagnetic field vector, namely, the geomagnetic heading but also the geomagnetic inclination, the system incident, and squint angle. It also confirms that the changing direction of the visible stripes in the mentioned Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data is mainly due to the variation of the geomagnetic inclination, while the geomagnetic heading is nearly constant along the orbit. Therefore, the denotation and presentation in that research that the projected geomagnetic field vector elongates in the direction of the stripe orientation might not be feasible.

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.

It is well known that spaceborne synthetic aperture radar (SAR) systems, which operate at L- and P-bands, are significantly influenced by the ionospheric effects. One of the severe effects is Faraday rotation (FR), which has a potential trend to cause the polarimetric distortion. Therefore, performing FR estimation is a prerequisite for the polarimetric SAR (PolSAR) application. A set of FR estimators have been proposed to solve this issue. Based on the Bickel and Bates's method, an improved FR estimator has been briefly mentioned, but has not been specified. The improved estimator's performance is verified by real SAR data, and experimental results indicate that the improved estimator shows the most robust performance in terms of system noise compared with other proposed estimators, which implies an applicable method for FR estimation.

The performance of spaceborne linearly polarized synthetic aperture radar (SAR) systems operating at low frequencies, such as L-band and P-band, is significantly affected by Faraday rotation (FR) effects. FR indicates the rotation of the polarization plane, which deteriorates the polarimetry accuracy of the polarization scattering matrix. A set of FR estimators have been proposed to solve this issue, but all estimators suffer from the ambiguity error of FR angle (FRA). Based on Bickel and Bates's estimator and the scatter distribution diagram of FR estimation, a novel correction method for eliminating FRA ambiguity error is proposed, which is divided into pixel-level correction and image-level correction. Experimental results verify the effectiveness of the novel correction method by processing the real data.

Phase synchronization is a key problem which has to be carefully considered in the distributed radar systems. In this letter, the use of a continuous duplex phase synchronization method for the distributed frequency modulated continuous wave synthetic aperture radar system is investigated. The bistatic raw signal model containing the phase difference due to the spatially separated oscillators is introduced. The continuous duplex phase synchronization procedure is proposed later and the synchronization method performance is predicted.

Synthetic Aperture Radar (SAR) system of high frequency, high resolution and high frame rate is one of the hot research areas. At present, the analysis of vibration error influence mainly embarks from the theoretical derivation, and simulated in level model. In order to accurately analyze the influence of high frequency vibration to high frequency SAR, it is necessary to simulate in signal level. Due to the short wave length of high frequency SAR, impact of high frequency vibration which is almost negligible to the conventional SAR of microwave band must be finely processed. Phase Adjustment by Contrast Enhancement (PACE) is a better SAR imaging autofocusing algorithm in the case of low Signal Noise Ratio (SNR), which can precisely estimate high frequency phase errors. In actual implementation, interpolation method is an effective means to improve the efficiency of the algorithm.

HRWS-SAR steers the antenna to increase the synthetic aperture which causes the radar changes beam squint angle gradually. The squint angle increases with the demanding of the resolution and swath width. With the squint increasing, the relative velocity between radar and lighting swath may reach several km/s. It causes the intra-pulse modulation on the echo. The traditional pulse compressing under the stop-go-stop assumption has bad performance including resolution decrease and PSLR increase. The paper established the echo model. The chirp rate is slightly changed because of the relative velocity. And velocity compensation method is put forward. The experiment results show that the pulse is well compressed. The PSLR and the resolution are restored to normal level.

The coherence and blind velocity are two important questions in multiple-input multiple-output (MIMO) radar ground moving target indication (GMTI). Multi-frequency signal is an effective way to solve the blind velocity problem, and multi-frequency signals are usually transmitted by MIMO radar. However, by this way the coherence is lost. This paper proposed an efficient way by circular-shift transmission of multi-frequency signals between pulses. Comparing to the traditional MIMO radar, the coherence of the same frequency is kept, the baselines become longer, and the blind velocity can be removed. The GMTI performance improvement is verified by a simulation.

Faced with an increasingly complex electromagnetic environment of space, Synthetic Aperture Radar (SAR) is susceptible to the influence of the Radio Frequency Interference (RFI) signals, thus seriously affecting the SAR image interpretation. This is even worse for the low-frequency-band SAR systems (P-band for example) which are disturbed by a lot of television broadcasting signals. Based on the model and characteristics of the RFI signals, a modified RFI suppression method is proposed. This method combines the advantages of traditional notch method and subspace projection method in SAR RF interference suppression. This not only reduces the false-alarm probability that introduced by the subspace projection method during the interference suppression process, but also provides a more accurate determination threshold and a better weight for notching, which can effectively enhance the interference suppression effect. The simulation results show that the proposed method is better than the subspace projecting method and the notch method.

This paper proposed an innovative bistatic radar system, named as GEO-UAV bistatic circular synthetic aperture radar (BCSAR). In GEO-UAV BCSAR, the geostationary satellite serves as the transmitter, while the receiver is mounted on the UAV platform moving along a circular trajectory. Combining the technical advantages of GEO SAR and that of UAV platform, GEO-UAV BCSAR benefits from low-cost, high operational flexibility and 3-D imaging ability. This paper introduced its system concept, and discussed its imaging ability and technical challenges. It is shown that the proposed GEO-UAV BCSAR could be able to provide a potential alternative for future remote sensing missions.

For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.

Two novel approaches for measuring the ionospheric total electron content (TEC) from single-polarized spaceborne SAR data were proposed in the mentioned paper, i.e., the TEC autofocus method and the systematic alternate up/down pulse methodology. The up/down pulse methodology indicates that ionospheric TEC could be estimated using the difference between range-compressed phase distributions of the up and down chirps. However, it can be shown theoretically that the difference between the up and down chirps after compression is not expected. Thus, the estimation of TEC from alternating up versus down chirp combinations might not be feasible.

This letter presents a novel extended space-time coding waveform scheme for multiple-input-multiple-output synthetic aperture radar implementation. The extensions include the fulfilling of the two Alamouti periods in one transmit duration minimizing the time-variant channel effect and the new form of the basic orthogonal waveforms. The main advantages of the proposed waveform scheme include ghost image prevention, cross-channel interference mutual cancelation, simultaneous transmission via orthogonal Tx channels, free selection of PRF, etc. The proposed techniques are verified by a simulation for the case of point-like targets.

This letter investigates the performance of a waveform-separation approach employing a posteriori digital beamforming (DBF) in elevation for multidimensional waveform encoding SAR sensing. The signal-to-noise ratio scaling factor in elevation and full-Doppler-band range ambiguity-to-signal ratio are defined to give global assessment of the DBF approach. Constraints for the selection of elevation subaperture number and their relevant influence on image quality are investigated. By theoretical analysis and simulation, the subaperture number can be appropriately selected by a tradeoff between the data volume and imaging performance. Further strategies for data reduction and performance optimization are briefly discussed.

Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR). FR angle (FRA) estimation is affected by additive noise, system imbalance, and cross-talk. Two new FRA estimators are proposed from linearly polarized covariance matrix data. Assumptions of these estimators are looser than reciprocity of the true scattering matrix, which is assumed for all the pre-existing estimators. The new estimators are validated by ALOS PALSAR full-pol data processing. Simulation results show that one of the new methods is sensitive to system amplitude imbalance, but possesses particularly high robustness to system phase imbalance. Hence, the proposed estimators can be candidates for FRA estimation according to the type and magnitude of the errors.

Ionospheric Faraday rotation (FR) introduces polarimetric distortion to trans-ionosphere signals. Spaceborne low-frequency wide-bandwidth synthetic aperture radar (SAR) signals may be distorted by FR effect because it is highly dispersive. Ionospheric polarimetric dispersion (PD) effects on spaceborne SAR imaging are modeled and analyzed. PD effect is included in polarimetric SAR system impulse response matrices. In particular, PD effects on linearly polarized and circularly polarized SAR systems are analyzed by system impulse response matrices. Theoretically, PD will introduce amplitude modulation to SAR spectrums and hence distort linearly polarized SAR imaging; for circularly polarized systems, it introduces relative image shift and phase errors between images of different channels. Numerical evaluations and simulations show that PD effects will introduce noticeable effects on future P-band wide-bandwidth SAR systems.

Terrain observation by progressive scans (TOPSAR) solves the scalloping effect of ScanSAR by steering the radar beam in the azimuth direction. The steering enables TOPSAR to achieve more qualified images than ScanSAR with the same coverage and resolution. Nevertheless, when TOPSAR is used for district observation, the steering enables it to get higher resolution and/or wider coverage. This letter compares the resolution limits of TOPSAR and ScanSAR and then proposes four possible modes of using TOPSAR for district observation. For each mode, the steering angle is discussed, and a design result is provided.

Azimuth multi-channel synthetic aperture radar has been widely studied to realize high resolution wide swath imaging. Spectrum reconstruction algorithm deals with the main-lobe aliasing while the side-lobe spectrum still exists and is weighted by the reconstruction network as well. In the present paper, azimuth ambiguity of multi-channel strip SAR is investigated, an analytical expression of azimuth ambiguity to signal ratio is derived based on established linear model of the multi-channel signal in Doppler domain. The analysis will definitely benefit multi-channel system design.

When bistatic synthetic aperture radar (SAR) works in alternating bistatic/ping-pong mode, it can perform phase referencing without a synchronization link between different satellites. The key to this is to extract a compensation phase from the bistatic echoes, including oscillator phase differences. An echo-domain phase synchronization algorithm using the correlation of bistatic echoes is proposed. Because of its larger equivalent synchronization frequency, the echo-domain algorithm has higher phase synchronization accuracy than the algorithm in the image domain. Experiments using simulated bistatic echoes with real single-stripmap SAR echo validate the proposed echo-domain phase synchronization algorithm.

To address the new requirements for InSAR raw-signal simulation for hardware-in-the-loop simulation application, a real-time raw-signal simulation algorithm with high accuracy is proposed. The raw signal is expressed as the convolution of the transmitting signal and the scene modulation signal (SMS), and the convolution is then implemented using the fast Fourier transform. The SMS is calculated by the position approximation in the time domain, and an interpolation technique is used to maintain the accuracy of the fast approximation algorithm. The SMS is filtered to the radar sampling frequency for real-time implementation. The approximation error of the fast algorithm is analyzed quantitatively to obtain the minimum interpolation factor. Ground-based hardware-in-the-loop simulation results are provided to validate the algorithm.

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 paper proposes a novel anti-jamming method for multichannel SAR. This method is based on the combination of Fast-time STAP and Pulse diversity. It can be used to reject the DRFM repeater deceptive jammer. The point-target simulation is used to show the working principle of the proposed algorithm and verify the proposed algorithm.

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.

An algorithm for achieving High-resolution wide-swath (HRWS) images in bistatic space-borne SAR with multiple phase centers is presented. Based on the analysis of the correlation of echos from all phase centers, a signal model is built that all echoes combined form a periodically nonuniform undersampled signal. Then the echoes with Doppler ambiguities are reconstructed to unambiguous uniform samplings which can be processed with conventional imaging algorithms. Simulation trials prove that the approach is valid and robust.

This letter presents a novel adaptive multiresolution technique to estimate the local fringe frequency in synthetic aperture radar (SAR) interferometric phase images. According to the coherent summation principle, the size and the shape of the interferogram spatial resolution for local frequency estimation are adapted pixel by pixel to track the topographic changes and optimize the space-frequency resolution. We show that, comparing conventional filters with the processing window of a fixed size (such as 7 times 7) and shape (such as a square), the proposed approach avoids the loss of phase fringes and provides a more accurate representation of the scene topography. In this letter, we present a method to eliminate invalid frequency estimation. A fast frequency estimation approach modified from the method proposed by Trouve et al. is also shown. Finally, both simulated data and actual SAR data are utilized to illustrate the effectiveness of the proposed method.

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.

An application-oriented spaceborne radar advanced simulator (SBRAS) is presented in this paper. SBRAS is initiated by the technical and economical requirements to verify formation-flying distributed satellites synthetic aperture radar (SAR) scheme and simplify the instrument hardware design. The simulator develops a full flow of signal processing including formation design, SAR raw data simulation of nature scene, imaging, InSAR processing, digital elevation model (DEM) generation and performance analysis. A user-friendly GUI is provided.