Frequency Synthetic Aperture Radar Research Articles

Estimation of lunar surface roughness using Chandrayaan-2 full-polarimetric DFSAR data

Polarimetric radar backscattering coefficients depend on the impinging frequency, dielectric constant, incidence angle, polarization, and surface roughness. Therefore, surface roughness can be estimated using high-resolution polarimetric radar datasets. For this purpose, we examine the utility of model-based and eigenvalue-based decomposition approaches in this work. While using model-based decomposition, the dielectric constant is estimated at the outset. Thereafter, the rms height is derived from a scalar multiplier that models surface roughness. We also propose a modified single-bounce eigenvalue relative difference (SERD) and establish that it is a better indicator of surface roughness than the circular polarization coherence and the original SERD for non-reflection-symmetric lunar terrain. The Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) datasets acquired over three Apollo mission landing sites are used for demonstration.

Simulation analysis of microwave emission from lunar subsurface for SAR radiometric mode dual frequency (L/S Bands) observations onboard Chandrayaan-2

Dual Frequency Synthetic Aperture Radar (DFSAR) (at L and S bands) onboard Chandrayaan-2 (CH-2) has an experimental radiometric mode operation. This paper evaluates the utility of such experimental observations of lunar studies for the future missions. Owing the deeper penetration of L and S bands frequencies, a multilayered microwave radiative transfer model coupled with thermal model has been used to account for the microwave thermal emission or the brightness temperature (TB) from the lunar subsurface at 1.25 GHz and 2.5 GHz frequency. As the dielectric property of lunar regolith is the primary deciding factor of depth of penetration of microwaves into lunar surface, the TB simulated using different dielectric models are compared with the observed TBs of Chang′E-2/Microwave Radiometer (MRM) and selected the dielectric model which consider both frequency and metallic content (Montopoli et al. (2011); Feng et al. (2020); Siegler et al. (2020)) in this study. DFSAR/CH-2 operating frequencies such as 1.25 GHz and 2.5 GHz can be used for sounding dielectric discontinuity due to the presence of water ice, high minerals or densely packed rocky medium than thermal sounding as microwave thermal emissions at these channels are not sensitive to surface temperature, as it is invariant with time for depth below 50 cm. Hence these frequencies are potential tool to study the sub-surface characteristics such as determination of lunar regolith thickness and for the discrimination of subsurface material type.

Integral equation modeling for dielectric retrieval of the lunar surface using Chandrayaan-2 fully-Polarimetric L-band dual frequency SAR (DFSAR) data

Our closest neighbor (Moon) is full of wonders and potentials that can help us learn more about the solar system and provide a habitat for space colonies. The lunar surface contains several potential volatiles implanted by the solar wind that can be used for In-Situ Resource Utilization (ISRU). Also by characterizing the lunar surface we can comprehend many solar system phenomenon. Radar has been proven to be very effective in characterizing and understanding the physical properties of the lunar surface. The success of Chandrayaan-2 (CH-2) in 2019 opened numerous doors to better understanding the lunar surface. The Dual Frequency Synthetic Aperture Radar (DFSAR), is the first L-band SAR ever used for lunar mapping. We approximated the dielectric values for the Permanently Shadowed Regions (PSRs) by combining the Integral Equation Model (IEM) and Artificial Neural Network (ANN). The two-dimensional Gaussian model proved to be more accurate when correlated with in-situ measurements made by the Apollo 17 mission out of four different configurations, but the validation process has its limitations, thus we analyzed the result from all the models. The real part of complex dielectric values of unknown craters near Malapert crater (PSRs SP 83260 0090770 and SP 841420 0059530) and Faustini crater (PSR SP 871460 0840750) were high in certain sections of the region and moderate in others. The high dielectric values of 11.2 and 10.9 were estimated by the two-dimensional Gaussian and Exponential model for the right-side walls of the PSRs SP 83260 0090770 and SP 841420 0059530, respectively. For three-dimensional Gaussian and Exponential models, the same region but with fewer pixels showed high dielectric values of approximately. However, the estimated dielectric value for the left side wall was around 3.4 for two-dimensional models and 2.3 for three-dimensional models. On the left side of the region, the PSR SP 871460 0840750 had high dielectric values of 10.6 and 10.2, as calculated by the two-dimensional Gaussian and Exponential models and approximately 13 for the three-dimensional models. The right side of the region showed approximately 3.6 dielectric values for two-dimensional models and 4 for three-dimensional models. We noticed that some pixel values showed ambiguous dielectric values (<1) during the retrieval and validation process, notably in three-dimensional models, which could be linked to incidence angle variability and model sensitivity. This indicates the model's limitation and is treated as outliers, which we rectified by optimizing the threshold. Overall, the research brought attention to the intriguing dielectric constant variation in PSRs.

Dielectric characterization and polarimetric analysis of lunar north polar crater Hermite-A using Chandrayaan-1 Mini-SAR, Lunar Reconnaissance Orbiter (LRO) Mini-RF, and Chandrayaan-2 DFSAR data

Studies of the lunar surface from Synthetic Aperture Radar (SAR) data have played a prominent role in the exploration of the lunar surface in recent times. This study uses data from SAR sensors from three Moon missions: Chandrayaan-1 Mini-SAR, Lunar Reconnaissance Orbiter (LRO) Mini-RF and Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR). DFSAR sensor is the first of its kind to operate at L-band and S-band in fully and hybrid polarimetric modes. Due to the availability of only L-band data out of the two bands (L-and S-band) for the study site, this study only used DFSAR's L-band data. The dielectric characterization and polarimetric analysis of the lunar north polar crater Hermite-A was performed in this study using Chandrayaan-1 Mini-SAR, LRO Mini-RF and Chandrayaan-2 DFSAR data. Hermite-A lies in the Permanently Shadowed Region (PSR) of the lunar north pole and whose PSR ID is NP_879520_3076780. Because of its location within the PSR of the lunar north pole, the Hermite-A makes an ideal candidate for a probable location of water-ice deposits. This work utilizes S-band hybrid polarimetric data of Mini-SAR and Mini-RF and L-band fully polarimetric data of DFSAR for the lunar north polar crater Hermite-A. This study characterizes the scattering mechanisms from three decomposition techniques of Hybrid Polarimetry namely m-delta, m-chi, and m-alpha decompositions, and for fully polarimetric data Barnes decomposition technique was applied which is based on wave dichotomy. Eigenvector and Eigenvalue-based decomposition model (H-A-Alpha decomposition) was also applied to characterize the scattering behavior of the crater. This study utilizes the hybrid-pol and fully polarimetric data-based Integral Equation Model (IEM) to retrieve the values of dielectric constant for Hermite-A crater. The dielectric constant values for the Hermite-A crater from Chandrayaan-1 Mini-SAR and LRO Mini-RF are similar, which goes further in establishing the presence of water-ice in the region. The values of the dielectric constant for Chandrayaan-2 in some regions of the crater especially on the left side of the crater is also around 3 but overall the range is relatively higher than the compact/hybrid polarimetric data. The dielectric characterization and polarimetric analysis of the Hermite-A indicatively illustrate that the crater may have surface ice clusters in its walls and on some areas of the crater floor, which can be explored in the future from the synergistic use of remote sensing data and in-situ experiments to confirm the presence of the surface ice clusters.

Improving Forest Above-Ground Biomass Retrieval Using Multi-Sensor L- and C- Band SAR Data and Multi-Temporal Spaceborne LiDAR Data

Due to the great structural and species diversity of tropical forests and limitations of the methods used to estimate aboveground biomass, there is uncertainty in quantifying its carbon sequestration potential. Measuring carbon sequestered in the terrestrial ecosystem and monitoring its dynamics is one of the key objectives in sustainable development goals. Synthetic Aperture Radar (SAR) has evolved as a key satellite technology in measuring and monitoring terrestrial carbon sink stored as biomass in plants. This study attempts to model forest above-ground biomass (AGB) using a random forest machine-learning approach where the predictor variables are from C-band (Radarsat-2), L-band (ALOS-2/PALSAR-2), and multi-temporal spaceborne LiDAR data from the GEDI platform. Training and validation data for the machine learning approach are obtained from the field measured inventory campaigns to evaluate the modeled forest biomass accuracies. The results show that variables from L-band (HH, HV), C-band (HV), and canopy height from the GEDI LiDAR platform performed effectively to model forest AGB with the coefficient of determination (R2) of 0.81 and root mean squared error (rmse) of 19.35 Mg/ha (%rmse – 17.17). In the case of single frequency SAR data, the analysis shows that the model derived from the L-band SAR data and LiDAR performed comparably better than the combination of C-band SAR and LiDAR data with an R2 of 0.78 and rmse of 21.36 Mg/ha (%rmse – 18.94). The results, thus, demonstrate the potential of SAR data (both single frequency and multiple frequencies) in combination with GEDI LiDAR data in effectively modeling AGB over highly biodiverse tropical forest regions.

Polarimetric analysis of L-band DFSAR data of Chandrayaan-2 mission for ice detection in permanently shadowed regions (PSRs) of lunar South polar craters

To confirm the presence of water on the moon, many scientists of the world are making continuous efforts through remote sensing data of different missions. In this direction, the Dual Frequency Synthetic Aperture Radar (DFSAR) sensor of the Chandrayaan-2 mission adds a very important chapter which is the world's first Planetary SAR mission of fully polarimetric capability with L- and S-band. This study utilizes the L-band fully polarimetric DFSAR data of Chandrayaan-2 mission for the PolSAR parameters-based analysis and ice detection in permanently shadowed regions (PSRs) of the lunar South Polar craters. The PSR IDs SP_875930_3125710, SP_837670_3387710, and SP_874930_3578760 of the lunar South Pole were selected for the polarimetric analysis using DFSAR L-band. Based on previous studies ((Li et al., 2018), two out of three PSR Ids (SP_875930_3125710 and SP_874930_3578760) were easy to identify for surface ice. That is why only two PSR IDs were used for polarimetric SAR analysis of DFSAR data for surface ice characterization and detection. The hybrid polarimetric simulation was also performed to the fully polarimetric L-band data to study stokes vectors and associated child parameters for the selected study area. The analysis of polarimetric distortions confirms the persistence of the polarimetric quality of the SAR data and for this, the polarimetric distortion analysis was performed with co-pol and cross-pol channels. Wave dichotomy-based Huynen decomposition and Barnes decomposition models were implemented to the fully polarimetric quad-pol DFSAR data. The eigenvalue-eigenvector-based decomposition model was also implemented to characterize the scattering behavior of the PSRs. A high correlation was obtained between Circular Polarization Ratio (CPR), entropy, and alpha for the 200 hundred points randomly collected from the image. Diversity index also showed a high positive correlation with CPR. The polarimetric quality of the data was evaluated with the scatterplot between the cross-polarimetric channels and it was observed that the L-band quad-pol data of DFSAR satisfies the criteria for PolSAR data of a monostatic SAR system. Analysis of the results obtained from the polarimetric SAR data indicated that the high volumetric scattering and CPR for the PSR ID SP_875930_3125710 may be due to ice clusters within the permanently shadowed region. Polarimetric analysis of the PSR (SP_874930_3578760) at Howarth Crater using L-band DFSAR data shows a low amount of volumetric scattering and a low CPR for most locations in the PSR. The different ranges of CPR and volume scattering for both craters indicate that polarimetric parameters and indices should be studied in conjunction with geomorphological parameters of the lunar surface, for unambiguous identification of surface ice clusters in the PSR.

Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images

This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

Comparison of Ocean Currents Derived From Dispersive Shifts in SAR Subaperture Image Sequences to ATI-SAR Surface Velocities

We report the initial results of an experimental investigation into the measurement of ocean surface currents with an ultrahigh frequency synthetic aperture radar (UHF SAR) that supports an along-track interferometric (ATI) mode. We compare results from two different approaches to the current measurement: analysis of wave dispersion in a time-sequence of subaperture images and standard ATI-SAR processing. We find that while both methods clearly show a surface velocity front over the north wall of the Gulf Stream, the change in velocity indicated by the dispersion method is significantly less than that indicated by ATI-SAR processing. We offer methodological and geophysical reasons for the observed differences.

Remote characterization of chloride content in oven-dried concrete specimens by using synthetic aperture radar image models

Synthetic aperture radar (SAR) imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete panel specimens (0.3x0.3x0.05 m3) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8% and 10% of cement weight). Five SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated SAR amplitude of back reflection Iintb, integrated amplitude Iint, average maximum amplitude I-max, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint shows the best performance. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude Iint, critical contour area Ac, and average maximum amplitude I-max shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parameters (Iint, Ac, and I-max) provides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.

Near field microwave subsurface imaging using metasurface loaded planar antenna and synthetic aperture radar (SAR) technique

A novel near field subsurface microwave imaging scheme is proposed by using a high gain planar metasurface (MS) loaded slot antenna integrated with the synthetic aperture radar (SAR) algorithm. The antenna structure here comprises a slot antenna integrated with the proposed MS designed using the epsilon very large (EVL) unit cell. The proposed MS-based antenna structure has a very low profile of 0.033λ, with a measured peak gain of 5.5 dB at 6.47 GHz. After adding two more additional MS layers, the peak gain of the resultant antenna is 12.1 dB. The broadside gain and efficiency are significantly enhanced with the incorporation of the EVL MS. Afterward, the antenna structure, along with the vector network analyzer and the 2-D automated scanner is employed for the near field imaging of test media by recording the scattering data along the target area. A spatial subwavelength resolution with a typical value of about 0.43λ is obtained at 1λ standoff distance after applying the single frequency SAR algorithm to the measured raw scattering data. The overall applicability of the proposed low-profile MS loaded antenna integrated with the SAR algorithm is demonstrated for the near field subsurface microwave imaging of various metallic and dielectric targets with subwavelength spatial resolution in free space and lossy media.

Ionospheric Phase Compensation for InSAR Measurements Based on the Faraday Rotation Inversion Method.

The ionospheric error can significantly affect the synthetic aperture radar (SAR) signals, particularly in the case of L band and lower frequency SAR systems. The ionospheric distortions are mixed with terrain and ground deformation signals, lowering the precision of the interferometric measurements. Moreover, it is often difficult to detect the small-scale ionospheric structure due to its rapid changes and may have more influence on ionospheric phase compensation for InSAR measurements. In this paper, we present a Faraday rotation (FR) inversion method and corresponding procedure to compensate the ionospheric error for SAR interferograms and to detect the variations of small-scale ionospheric disturbances. This method retrieves the absolute total electron content (TEC) based on the FR estimation and corrects the ionospheric error for synthetic aperture radar interferometry (InSAR) measurements by transforming the differential TEC into the ionospheric phase. In two selected study cases, located in high latitude and equatorial regions where ionospheric disturbances occur frequently, we test the method using the Phased Array L-band Synthetic Aperture Radar (PALSAR) full-polarimetric SAR images. Our results show that the proposed procedure can effectively compensate the ionospheric phase. In order to validate the results, we present the results of ionospheric phase compensation based on the split-spectrum method as a comparison to the proposed method. To analyze the ability of our proposed method in detecting small-scale ionospheric disturbances, TEC derived from FR estimation are also compared with those derived from the global ionosphere maps (GIM). Our research provides a robust choice for the correction of ionospheric error in SAR interferograms. It also provides a powerful tool to measure small-scale ionospheric structure.

Correction of amplitude scintillation effect in fully polarimetric SAR coherency matrix data

In this paper, we investigate the correction of ionospheric amplitude scintillations appearing as image artifacts in low frequency synthetic aperture radar (SAR) data. A method has been proposed to separate amplitude and phase components of fully polarimetric SAR (POLSAR) coherency matrix [T], and then implement a combination of multi-level, two-dimensional Discrete Wavelet Transform and Fast Fourier Transform (DWT-FFT) to correct the amplitude scintillation-induced stripes in [T] matrix. In the methodology, [T] is decomposed into component matrices of amplitude and relative phases. To the amplitude [T] matrix, a combination of 2-D DWT is performed, using the Haar wavelet, followed by a non-linear 2-D FFT correction strategy to remove amplitude scintillation stripes. Using DWT, each amplitude [T] matrix element is decomposed into low- and high-frequency components known as approximate and detailed coefficients respectively. On applying a 2-D FFT on the detailed coefficients, amplitude scintillation-induced stripes are easily identified in the frequency spectrum and then removed using 2-D FFT correction strategy of averaging-post-thresholding. The proposed method is tested on the [T] matrix of several POLSAR datasets acquired from Advanced Land Observation Satellite/Phased Array type L-band synthetic aperture radar (ALOS/PALSAR) and Advanced Land Observation Satellite-2/Phased Array type L-band synthetic aperture radar-2 (ALOS-2/PALSAR-2) satellites. The performance of the correction technique is analysed both, qualitatively and quantitatively. While improvements in the post-correction datasets are visually observed, a supervised Wishart classification also performed on the [T] matrices of undisturbed (reference), disturbed and corrected data indicates significant increase in overall accuracy (OA) and kappa coefficient (k̂) parameters. The classification accuracies of the disturbed data (OA = 69.31%, k̂=0.54) improved to OA = 76.49%, k̂=0.68 after applying the proposed correction approach, which is comparable to the reference data values (OA = 81.57%, k̂=0.71). The proposed method performs significantly better than the existing technique of a simple 2-D FFT approach (OA = 73.49%, k̂=0.62). The contribution of dominant scattering powers, obtained from the seven-component scattering power decomposition of POLSAR coherency matrix (7SD) model, also demonstrates post-correction improvements. The dominant scattering power for targets increased by ~3–9% using the proposed approach, shows an improvement of ~3% over the existing 2-D FFT technique.

Indoor THz SAR Trajectory Deviations Effects and Compensation With Passive Sub-mm Localization System

In radar remote sensing, the Terahertz (THz) spectrum is presently being investigated worldwide with focus on short-range indoor and outdoor applications. The spectrum broadens the unmanned aerial vehicle (UAV) based synthetic aperture radar (SAR) applications to indoor room profiling with sub-mm resolution and material characterization as many materials have unique fingerprints at this spectrum. SAR technique requires precise localization information of the mobile radar sensor, which in conventional SAR is achieved using an existing localization infrastructure, such as a global positioning system (GPS) and inertial measurement unit (IMU). For the indoor THz SAR, the GPS does not provide coverage in indoor complex environments, and also the state-of-art compact IMU does not provide the required sub-mm accuracy. These limitations can be overcome by utilizing an indoor localization system. Therefore, this paper presents an indoor THz simultaneous localization and mapping (SLAM) system. The system comprises of passive tags based radio frequency identification (RFID) localization system and SAR that provides the UAV localization and mapping of the in-room objects. Another challenge for the UAV based indoor THz SAR that is addressed in this paper is motion compensation (MOCO). At the THz, MOCO requires special consideration due to very small trajectory deviation is in the range of carrier wavelength. Therefore, to study the effects of the sub-mm translational errors, a testbed has been set up, and measurement results are presented in this paper along with the 3D electromagnetic simulation results for a carrier frequency of 275 GHz and bandwidth of 50 GHz. Further, to compensate these errors, the sub-mm localization system is used and the results are presented to validate the proposed solution for indoor THz SAR MOCO.

An Improved Generalized Chirp Scaling Algorithm Based on Lagrange Inversion Theorem for High-Resolution Low Frequency Synthetic Aperture Radar Imaging

The high-resolution low frequency synthetic aperture radar (SAR) has serious range-azimuth phase coupling due to the large bandwidth and long integration time. High-resolution SAR processing methods are necessary for focusing the raw data of such radar. The generalized chirp scaling algorithm (GCSA) is generally accepted as an attractive solution to focus SAR systems with low frequency, large bandwidth and wide beam bandwidth. However, as the bandwidth and/or beamwidth increase, the serious phase coupling limits the performance of the current GCSA and degrades the imaging quality. The degradation is mainly caused by two reasons: the residual high-order coupling phase and the non-negligible error introduced by the linear approximation of stationary phase point using the principle of stationary phase (POSP). According to the characteristics of a high-resolution low frequency SAR signal, this paper firstly presents a principle to determine the required order of range frequency. After compensating for the range-independent coupling phase above 3rd order, an improved GCSA based on Lagrange inversion theorem is analytically derived. The Lagrange inversion enables the high-order range-dependent coupling phase to be accurately compensated. Imaging results of P- and L-band SAR data demonstrate the excellent performance of the proposed algorithm compared to the existing GCSA. The image quality and focusing depth in range dimension are greatly improved. The improved method provides the possibility to efficiently process high-resolution low frequency SAR data with wide swath.

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued.

A novel imaging method for random stepped frequency SAR with low SNR

ABSTRACTThis letter proposes a novel structured compressive sensing (CS) imaging method in random stepped frequency (RSF) synthetic aperture radar (SAR) for better imaging performance in the presence of strong additive noise. In this method, the multiple measurement vectors (MMV) model is established in range-Doppler domain with range migration taken into consideration and a modified sparse recovery algorithm is derived to synthesize the one-dimension range profile. Traditional cross-range focusing algorithm is then performed to obtain a well-focused image. The proposed method makes full use of the coherent information among different bursts, which may further illuminate the influence of additive noise and decrease the number of transmitted pulses needed in one burst. The simulated results demonstrate the validity of the proposed method.

Precise Calibration of Channel Imbalance for Very High Resolution SAR With Stepped Frequency

Synthetic aperture radar (SAR) images require a high-resolution system for accurate interpretations. This high range resolution can be achieved by stepped frequency chirp signals. To reconstruct a wideband waveform from each subband signal, amplitude/phase/delay imbalance between the channels should be precisely compensated. In this paper, the system configuration is first presented. Further, a calibration strategy was proposed based on three calibration loops: the reference calibration, transmitting calibration, and receiving calibration loops for coarsely compensating the channel imbalance. Then, two different methods based on the cost functions were proposed to remove the residual channel imbalance. The proposed methods were validated using stepped frequency SAR data acquired by an X-band airborne SAR system with a total bandwidth of 3.6 GHz, yielding (unweighted) a 3-dB range resolution of 4 cm.

The Stability of UWB Low-Frequency SAR Images

This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.

IMAGING ENHANCEMENT OF STEPPED FREQUENCY RADAR USING THE SPARSE RECONSTRUCTION TECHNIQUE

Based on the observation that sparsity assumption is well satisfled in the synthetic aperture radar (SAR) imaging applications, there is increasing interest in utilizing compressive sensing (CS) in SAR imaging. However, there are still several problems which should be concerned in CS-based imaging approaches. Firstly, inevitable noise and clutter challenge the performance of CS algorithms. Secondly, the super-resolving ability of CS algorithms is not su-ciently exploited in most cases. Thirdly, nonideal characteristics of mutual coherence afiect the performance of CS algorithms in complex scenes. In this paper, a novel CS imaging framework is proposed for the purpose of improving the imaging performance of stepped frequency SAR. Meanwhile, a super-resolving imaging algorithm is proposed based on the nonquadratic optimization technique. Simulated and rail SAR measured data are applied to demonstrate the efiectiveness of the novel framework with the proposed super-resolving algorithm. Experimental results validate the superiority of this method over previous approaches in terms of robustness in low SNR, better super-resolving ability and improved imaging performance in complex scenes.

Improved Stepped-Frequency SAR Imaging Algorithm With the Range Spectral-Length Extension Strategy

In order to achieve a high range resolution and low instantaneous bandwidth, stepped frequency synthetic aperture radar (SAR) is widely used with stepped-frequency chirps and frequency-domain spectrum reconstruction method. To combine the spectrum reconstruction with SAR image algorithms, “Image for each sub-band and then reconstruct the wideband spectrum” is a more adaptable flow since its validity for almost all imaging algorithms. However, due to the two-dimensional (2-D) spectrum twisting, a special problem called range spectrum aliasing may occur and lead the flow to invalidity, especially in high-resolution SAR systems. In this paper, we discuss the extended range spectrum by the analysis of the 2-D twisting spectrum, judge its influence through an azimuth threshold resolution, and propose an improved stepped-frequency SAR imaging algorithm with the range spectral-length extension (RSLE) strategy, which can eliminate the range spectrum aliasing. After theoretic analyses, simulations and experimental results are provided to demonstrate the proposed algorithm.