As one of the key performance indicators of SAR sensors, resolution, which is determined by the SAR signal bandwidth and plays a very important role in the interpretation of SAR images. The transformed images of high-resolution SAR images at different resolutions contain different layers of information, which can provide complementary resolvability for ship target extraction. In this paper, experiments and analyses are conducted on the effects of multi-resolution SAR images on the extraction accuracy of four parameters: length, width, principal axis angle and geometric center of ship targets. It is concluded that there is an influence on the geometric feature parameters of the ship targets of different resolution SAR images, and for a certain type of typical targets, there exists a critical resolution, which makes the extraction of the relevant parameters with the highest accuracy and the most stable performance.

In the target detection task in synthetic aperture radar (SAR) imagery, the target may become defocused due to its own motion, leading to detection failure. To address the issue of SAR motion-defocused target detection, this paper first uti-lizes the millimeter-wave radar inverse SAR (ISAR) imaging method to collect and construct an ISAR moving vehicle detection dataset (IMVDD), then proposes a range-Doppler (RD) map guided detection network (RDM-Net). The ISAR images demonstrate that vehicle targets are azimuthally de-focused and significantly affected by clutter, presenting as a composition of discrete strong scatterers. Based on YOLOv8 model, an azimuthal spatial attention constructed from range-Doppler map is introduced to utilize the implicit motion information in SAR data to aid the localization of targets. Moreover, the coordinate attention (CA) is introduced to better extract detailed and context information from SAR scattering features; then a modified asymptotic feature pyramid network (AFPN) is introduced to more effectively facilitate fusion the feature maps of different layers. Experiments conducted on the proposed IMVDD dataset demonstrate that the proposed method can effectively enhance defocused target detection performance.

Since the dynamic range of synthetic aperture radar (SAR) data is extremely large, SAR data quantization is required for storage and display of SAR images. The quantization process may cause undesirable change in the characteristics of the targets on the images, making it challenging to effectively detect targets in deep learning-based target detection task. To address this problem, a multi-quantization-based detection method is proposed in this paper. First, the effect of different quantization method is analysed and a multi-quantization based data augmentation strategy is proposed. Second, the labeling of SAR targets is analysed in response to the inconsistency between target scattering characteristics and physical contour shapes and the issue of partial visiblity. Then, the multi-quantization-based detection method is proposed to obtain more stable and complete detection results. The experiments conducted on the AIR-SARShip dataset demonstrate the effectiveness of the proposed method.

With the advantages of short revisit time and wide coverage, geosynchronous orbit spaceborne radar (GEO-SBR) is expected to be utilized for early warning tasks. However, GEO-SBR also encounters challenges, such as the "stop-and-go" assumption not being applicable and the significant impact of Earth’s rotation. This paper introduces the geometry under the "non-stop-and-go" assumption, analyzes the clutter space-time distribution considering the Earth’s rotation effects, and proposes a clutter suppression method based on attitude steering. Simulation results represents the clutter space-time distribution, and validates the effectiveness of the clutter suppression method.

In order to obtain the high resolution of two-dimensional azimuth and pitch direction, two-dimensional MIMO array is used for vehicle-mounted 4D millimeter wave radar. At present, two-dimensional MIMO millimeter wave radar arrays still have problems such as high sidelobe and grating lobe exceeding main lobe, which can not accurately identify target angle in detection process. Based on the current requirement of angle measurement, a design method of two-dimensional MIMO array is proposed. The array arrangement of low sidelobe and low grating lobe imaging effect is obtained by filtering the element spacing and restricting the distribution position of array elements. Finally, several different array configurations are discussed by simulation to prove the feasibility of the proposed method.

Synthetic Aperture Radar (SAR) target image acquisition presents challenges and incurs high annotation costs. The emergence of self-supervised contrastive learning shows promise for SAR automatic target recognition (ATR) with limited data. However, SAR images suffer from poor discriminability and high sample similarity, hindering instance discrimination in contrastive learning. To address this, we propose Low-confidence Discriminant Contrastive Learning (LDCL), which integrates group-instance contrast and batch mixed training for SAR ATR. LDCL consists of two branches: classical instance discrimination and group-instance discrimination. We refine the SAR-group instance discrimination loss function by incorporating distance calculations to guide feature vectors towards nearest clusters, enhancing discrimination within the feature space. Additionally, we introduce a batch image mixing training strategy to reduce confidence in SAR instance discrimination while preserving intra-class consistency. Experimental results on small sample MSTAR and FUSAR-Ship datasets demonstrate that LDCL outperforms traditional transfer learning and self-supervised learning methods, achieving significantly higher recognition rates in SAR ATR tasks.

No-line-of-sight perception is a fundamental and challenging problem due to the complex environment and high demands in detection tool. In recent years, the upsurge of autonomous driving has made the mm-wave radar hardware more mature and convinent. Contemporary NLOS detection approaches using signal processing technique mainly focus on eliminating interference from extranous signals and recover the detailed images of NLOS scenes. In this paper, a unified and all-in-one network is proposed which directly deals with mmwave radar received signal and extrats the effective object’s state information in the signal. We construct a mm-wave dataset with Ti’s 77GHz mm-wave radar to train and evaluate our network. Experiments on the dataset show that our network has a good performance on the NLOS objects. And it can easily expanded for NLOS moving objects tracking task.

Performance of conventional algorithms for along-track baseline multichannel synthetic aperture radar (SAR), which deal with azimuth ambiguities, ground moving target indication (GMTI) or interference, degrades severely for hybrid-baseline multichannel SAR. Space-variant interferometric phase, due to terrain elevation and hybrid-baseline, destroys the consistency among Doppler spectrum, which is the foundation of these algorithms. An image-domain signal suppression method designed for hybrid-baseline multichannel SAR is presented in this letter. With prior knowledge about digital elevation model (DEM), the proposed method recognizes desired signal on each pixel stack and suppresses others by image-domain digital beamforming (DBF), which gives out a reconstructed image. Signal classification is achieved by pixel-wise direction of arrival (DOA) estimation in image domain. Error in DEM is also considered and analyzed, which guides the signal classification. Simulation with ambiguities and two types of interference are presented, verifying the proposed method.

Abstract—Auto-registration video synthetic aperture radar (ViSAR), which requires real time pixel index unifying and resolution matching, is of great significance due to its applicability in multi-aspect observation and continuous monitoring. The phase error induced by the wavefront planar assumption, however, varies with different ViSAR frames, which limits the size of auto-registration imaging scene. To enlarge the auto-registration imaging swath, a parameter-adjusting auto-registration overlapped subaperture algorithm (PAAR-OSA) is proposed in this paper. By collaboratively designing the subapertures within each frame and among different frames in the stabilized-scene coordinate and cooperatively compensating the phase error of all frames, auto-registration with larger imaging swath can be achieved. Both the point targets and distributed targets validation results verify the superiority of the proposed method compared with existing algorithms.

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) may benefit from reconstructing signals in the image domain, for taking local characteristics of the scene into account. An image-domain signal model for HRWS SAR to reconstruct nonambiguous images is presented in this article. This model makes the idea of controlling the regional reconstruction performance and taking the advantage of nonuniform scatter distribution possible. Resolution is also preserved perfectly by the proposed model despite whatever tradeoff is made between the azimuth-ambiguity-to-signal ratio (AASR) and the signal-to-noise ratio (SNR). The model is derived on backprojection (BP) images and can deal with different squint angles and beam steering strategies, thanks to the BP algorithm. Although the reconstruction is done pixel by pixel, the computational burden, which depends on beam steering and the specific constraints in reconstruction, may not severely increase. Two methods for Spotlight SAR and two methods for Stripmap SAR are also presented based on the proposed model to verify the model and demonstrate its potential. Simulations on both the point target and extended target with different squint angles are carried out to verify the proposed methods, which also verify the proposed model indirectly.

The ground-based arc synthetic aperture radar (ArcSAR) can realize panoramic observation by utilizing the circular motion of the antenna. Since the circular trajectory of the antenna brings complicated high-order range-azimuth coupling in echo signal, far-field approximation, reference range approximation, or second-order series approximation to the range model are commonly applied for the conventional fast imaging algorithms to decouple the range-azimuth coupling. However, when a wide-beam millimeter wave radar system is adopted to realize centimeter-level high-resolution imaging for near-field observation, these approximate treatments will cause severe defocusing in the image. In this paper, an improved range-Doppler (RD) imaging algorithm is proposed. The high-order Taylor series approximation to the range model is adopted to meet the error requirement in the near-field wide-beam condition. By using the series inversion method, the analytical expressions for range migration and azimuth matched filter in range-Doppler domain are derived to achieve range-azimuth decoupling and precise azimuthal focusing within the whole range swath. Simulations and real-data experiments demonstrate that the proposed algorithm can achieve fast and accurate imaging which can greatly support the millimeter wave ArcSAR in near-field observation applications.

This paper applies the overlapped subaperture algorithm (OSA) for geosynchronous (GEO) spaceborne/missile-borne bistatic SAR (GEO SMB-BISAR) imaging. Compared with the original bistatic SAR, GEO SMB-BISAR performs better in flexibility and security. The essence of the OSA is to perform the imaging process twice by azimuth subaperture division. The quadratic phase errors (QPE) induced by the planar wavefront assumption can be compensated by two steps imaging processing within and between subapertures. The OSA can expand a wider imaging swath than the polar format algorithm (PFA). Validation results demonstrate the validity of the OSA methodology.

Insufficient data of SAR target recognition task leads to low accuracy and poor generalization of model and the SAR imaging mechanism leads to the insignificant difference between ship targets, which make recognition difficult. To overcome the above problems, we propose a SAR maritime method using siamese networks for model pre-training. Siamese network produce sample pairs to ease training sample insufficiency, and output difference of sample pairs to help model learning heterogeneous difference. Then, transfer the pre-training parameters of feature exaction layer to an end-to-end model. Finally, the end-to-end convolutional neural network is obtained by fine-tuning the parameters with supervised information. Experimental results show that the SAR maritime target recognition method based on siamese network training can effectively improve the recognition accuracy under the training condition of a small number of samples.

Most of the interferometric synthetic aperture radars (InSARs) operate in zero-squint or low-squint geometry. A spatial phase ramp in focused squint SAR images leads to a higher demand for coregistration, which limits the application of InSAR. This letter investigates InSAR processing with a high squint angle. The spatial phase ramp is here directly used for the construction of the digital elevation model (DEM). The method based on back projection algorithm (BPA) is analyzed first. Then the form of the interferometric phase for BPA is derived with the existence of squint, proved to share the same form as conventional InSARs. The restriction that relates to coherence is also discussed. A processing flow without coregistration is finally presented. Simulated two-channel SAR data validates the proposed method.

Video synthetic aperture radar (ViSAR) is gaining increasing attention in the remote sensing area due to its advantages in continuous observation compared with conventional synthetic aperture radar (SAR). Generally, the polar format algorithm (PFA) is utilized to generate the ViSAR frames considering both processing efficiency and imaging quality. However, the changes in squint angles for different ViSAR frames will cause variations in target positions when the classic fixed-parameter PFA is utilized. Hence, an image registration step is typically needed, which greatly increases the computational load. Meanwhile, the azimuth resolution also deteriorates rapidly for the ViSAR frames as the squint angle becomes larger. In order to solve the aforementioned problems, a parameter-adjusting autoregistration PFA (PAAR-PFA) is proposed for the ViSAR. By adjusting system parameters, such as carrier frequency, pulsewidth, and sampling frequency, according to the azimuth sampling positions, PAAR-PFA can achieve autoregistration for ViSAR frames without range interpolation and geometric correction in fixed-parameter PFA, which significantly improves the processing efficiency. At the same time, since the azimuth sampling number of frame data is adjusted with the squint angle, the variation range of the azimuth resolution under different squint angles is considerably reduced. Point target and extended target simulations confirm the feasibility of the proposed algorithm.

Interferometric Synthetic aperture radar (InSAR) aided inertial navigation system (INS) is developed to introduce terrain elevation into registration, which gives out accumulated error of INS. Unwrapped InSAR interferogram plays a key role in previous InSAR/INS frameworks, while airborne and side-looking assumptions are usually made. However, phase unwrapping is time-consuming and high squint geometry with nonlinear trajectory is common for applications with InSAR/INS. In this article, a novel wrapped InSAR interferogram based positioning method for high-speed maneuverable platform is proposed. A novel back-projection based InSAR (BP-InSAR) signal model for slightly curved diving trajectory with high squint angle is firstly presented. Then process which generates phase gradient template from given DEM is presented, considering both the estimated and measured trajectories. Error analysis is also made to discuss the performance of the proposed method in single dual-channel observation. Finally, a sequential maximum likelihood estimator based on error analysis is implemented to deal with the remained uncertainty in single observation. Numerical experiments verify the proposed method and fully demonstrate the performance of it with curved trajectory and random errors in both position and attitude.

In recent years, impressive performance of deep learning technology has been recognized in synthetic aperture radar (SAR) automatic target recognition (ATR). Since a large amount of annotated data are required in this technique, it poses a trenchant challenge to the issue of obtaining a high recognition rate through less labeled data. To overcome this problem, inspired by the contrastive learning, we proposed a novel framework named batch instance discrimination and feature clustering (BIDFC). In this framework, different from that of the objective of general contrastive learning methods, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Consequently, our flexible framework is equipped with adjustable distance between embedding, which we term as weakly contrastive learning. Technically, instance labels are assigned to the unlabeled data in per batch, and random augmentation and training are performedfewtimes on these augmented data. Meanwhile, a novel dynamic-weighted variance loss (DWV loss) function is also posed to cluster the embedding of enhanced versions for each sample. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data. Even though a linear evaluation is performed on the same training data, the accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in OpenSarShip database, indicating that our method can be generalized to other data sets. Our code is available at:https://github.com/Wenlve-Zhou/BIDFC-master.

Due to the complexity and inaccuracy of the force model of low earth orbit(LEO) satellite, the ephemeris data is not accurate enough for translation compensation, which will cause the residual translation component error, then leading to the dissatisfaction of the range and azimuth resolution for imaging. To face the problem above, the secondary correction of translational component is carried out by updating the ephemeris data fitted by the ephemeris data calculated by orbit two lines elements(TLE) and center slant which is estimated with the real echo data of the space station through correlation method, after a coarse correction-the registration of ephemeris data and echo data-and then the Inverse synthetic aperture radar(ISAR) image of the space station is obtained by combining the polar format algorithm(PFA), providing the foundation for monitoring, identification and tracking of space targets. The imaging results prove the validity of the method, which can be referred to in the imaging of LEO satellite and the real data processing based on ephemeris data.

Ship detection is one of the important ocean applications of radar images. However, researches for ship detection in low-resolution images are relatively scarce. To improve the ship detection performance in low-resolution conditions, the paper adopts the range-Doppler (RD) images and takes advantage of the multi-frame information with the long short-term memory (LSTM) network. In this paper, the interpolation method and the LSTM method are proposed, which have the advantages of speed and precision respectively and show strong anti-interference ability.

Tropical natural forest plays an important role in environmental change and biodiversity researches. However, the complexity of structures and its cloudy and rainy environment make tropical natural forest classification difficult. Taking Hainan, China as study area, we conduct a tropical natural forest classification study by combining multi-temporal synthetic aperture radar (SAR) images collected from Sentinel-1 satellite and optical images collected from Landsat-8 satellite in this paper. The backscatter coefficient, spectrum information, digital elevation mod (DEM), temporal information offered by multiple remote sensing data have been analyzed to identify the evergreen and deciduous broad-leaved forest, evergreen coniferous forest, tropical monsoon forest, typical tropical rain forest and other forest types. In addition, a two-stage tropical forest classification strategy is proposed based on support vector machine (SVM) classifiers, namely the primary land cover type classification and tropical natural forest type classification in which the time-series backscattering information is used. Finally, the Hainan tropical forest mapping image is obtained based on the proposed classification strategy and the overall accuracy reaches to 90% based on field survey data. The results show the effectiveness of the classification strategy on tropical natural forest classification.

Based on Sentinel-1 EW mode data and GEE platform, this paper proposes an Antarctic ice sheet freezing and thawing detection method based on change detection and decision tree. On the GEE platform, first of all, the median value of the Sentinel-1 data in the winter months of June, July and August is as a base image set, then the difference between the summer and the base image of the same orbit is detected. At last, the thresholds of both the elevation and the backscattering coefficient difference are used to judge the freezing state of the Antarctic ice sheet. Use this method, the monthly update information on the freezing and thawing of the Antarctic ice sheet from October 2016 to March 2017, and from October 2017 to March 2018 are obtained. For the detection results, the accuracy of the self-selected samples and automatic weather station data is used for verification. The average overall accuracy of the self-selected sample verification is 93%, the average Kappa coefficient is 0.90, and the average accuracy of the automatic weather station verification is 83.86%.

This paper studies positioning issue in navigation using InSAR concept. A novel method based on wrapped interferometric phase registration, which differs from common InSAR based navigation method, is proposed. This method may be useful when navigating throw hills or areas with low SAR image characteristics but rich terrain elevation features. Accurate DEM data is needed to generate ideal interferometric phase template for registration. Three dimensional positioning is achieved by estimating vertical bias and horizontal bias in order. The proposed method works well under different squint angle. Simulations are carried out to verify the method. Precision of the method under different squint angle is also exhibited in simulations.

Remote sensing images have different levels based on spatial resolution, which will affect the object detection performance seriously. This paper quantitatively analyses the impact of Google Maps’ level on object detection, taking the transmission tower as an example. The object area proportion (OAP) index is defined to help choose the data used for rapid detections and is capable of obtaining the optimal results under the particular requirements of speed and accuracy when observing a specific object in remote sensing images.

The spatial localization of interference source is a critical procedure in Radio frequency interference (RFI) suppression. It is not suitable to use traditional multi-station interference localization methods in most synthetic aperture radar (SAR) systems. This paper proposes a novel method of single RFI localization based on conjugate cross-correlation of dual-channel SAR signals. First, interference detection technology is applied to check whether echo signal is interfered. Then, conjugate cross-correlation is performed on SAR signals, and the distance difference between interference source and receiving channels is calculated. Finally, spatial localization equations of interference source are established, and optimization algorithm is applied to solve the coordinates of interference source. The results of simulation experiments illustrate the effectiveness of the proposed method.

Efficient source coding is desired for any data storage and transmission. It could be enabled by adopting a transform inspired by natural phenomena. Based on the mechanical vibration models, a family of bases applicable to data compression is constructed. The eigenvectors of vibrating thin square plates, which are composed of sinusoidal and hyperbolic functions, are used to construct these real-orthonormal bases. Our analyses show that their attributes and performance are comparable to those of the widely used Discrete Cosine Transform. Since compressive sensing is a way to build the data compression directly into the acquisition, we propose to apply the set of low-frequency atoms of these bases that carry the main portion of the signal energy as sensing patterns. Thus, the measurement ensemble contains the high-energy Sinusoidal-Hyperbolic Transform's (SHT's) coefficients of the scene under-view. This sampling method leads to high fidelity reconstruction along with good efficiency in encoding and decoding. The dictionary matrix that is made by the SHTs and a non-trigonometric sparsifying basis like Hadamard is well-conditioned for the pursuit algorithm. Both subjective and objective evaluation of the reconstruction results validates the effectiveness of our method. Compared to the random Gaussian sensing patterns, for the same Compression Ratio (CR), the proposed sampling method results in images with significantly higher fidelity. The sensing patterns are also shown to be robust against Gaussian and Poisson noise. Application of our scheme to image compression is also discussed.

Frequency Modulated Continuous Wave (FMCW) is an alternative to pulsed mode operation of Synthetic Aperture Radar (SAR). It has advantages of less power requirements, low mass, low cost, and therefore simpler system; but the disadvantage is requirement of separate antennas for transmission and reception. Interrupted FMCW can use a single antenna by interleaving transmission and reception, but the disadvantage is discontinuity of data in azimuth which, after processing leads to the appearance of unwanted spikes paired with the compressed pulse. This paper presents a solution for the one-dimensional azimuth processing of interrupted data. An all-pole model is fitted on each individual segment of deramped azimuth data, and spectrum is estimated which reveals the target locations corresponding to that segment. Finally, the spectrum of all the segments is added to obtain a complete target profile in azimuth. Three different methods have been used for calculating model coefficients, and the results have been compared. It is shown that covariance method and Burg's method give sharp peaks for target locations.

In this paper, we combine deep learning with radar image processing to explore the influence of different quantization methods on the final detection performance of the radar image subjected to strong points after different quantification methods. Considering problems caused by the characteristics of SAR image data, the LeNet network model in deep learning was used to train and verify the quantified radar images respectively. The impact of different quantization methods on SAR image classification and detection was analyzed. The most friendly way to quantify the actual radar images was explored. Radar image target detection based on depth learning provides the basis for exploration.

With increasing development of Synthetic Aperture Radar (SAR) jamming technology, the jamming effects analysis for some innovative SAR, such as Multi-channel and Multi Input and Multi Output (MIMO) SAR, is significant. The geometry model of Multi-Channel and MIMO SAR are introduced. Meanwhile, the jamming blanket factor is researched and the barrage jamming is simulated. Simulations demonstrated jamming effects for both Multi-Channel and MIMO SAR.

Suppression technology of barrage jamming is an important approach to ensure the normal operation of the synthetic aperture radar (SAR) system. The detection and classification of jamming is a necessary procedure in this technology. Unsuitable thresholds set in the traditional methods may reduce the detection accuracy. In order to avoid it, this paper proposes a new method of barrage jamming detection and classification for SAR based on convolutional neural network (CNN). The signal model is constructed based on the statistical characteristics of the SAR echo signal. Based on this, a data set containing echo signals and interference signals is generated by simulation. Finally, the convolution neural network VGG16 is used to detect whether the signals in the dataset is contaminated by barrage jamming and identify the type of the interference. The experiment result illustrates that the VGG16 network trained by the frequency domain signals can effectively detect and classify the jamming signals.

SAR image segmentation is the pre-process for SAR image application. This paper presents a new SAR image segmentation algorithm combining both the the bias field and Markov random field(MRF) characteristic with fuzzy clustering model(FCM) called BMFCM. The MRF characteristic of an image includes the spatial information of the image and the bias filed estimation is introduced to deal with the grey intensity inhomogeneity in SAR image. Considering the specific features of SAR images, the experiment results on real SAR images demonstrate the validation of the proposed algorithm.

Deceptive jamming, covering the true targets and producing false targets in the SAR image, has been widely applied in electronic warfare. To effectively suppress the deceptive jamming generated by detecting parameters of target echoes, this work proposes a novel anti-jamming approach. Firstly, the chirp signal with varying initial phase is predesigned and transmitted. Then, the random initial phases of received echoes are removed before imaging. The imaging outputs of the deceptive jamming and real signal are derived through theoretical analysis. By comparing those signal models, we obtain that the proposed method induces the defocusing of false signal while the real signals are well focused. Finally, the performance of deceptive jamming suppression method is validated by simulation.

In this paper, the authors propose sector scan ground-based synthetic aperture radar (GB-SAR) technique for real-time monitoring of wide-ranging landslides. The non-linear movement of the antenna which moves along a sector to form a wide scanning area makes the azimuth properties complicated. Geometric model of the antenna movement is established to derive the slant range expression. Further, the azimuth properties of the new mode are especially investigated, containing calculation and discussion of azimuth bandwidth and resolution. Imaging simulation of point target and point array target are carried out, whose result can agree with the analysis above well showing the feasibility of the model and the correctness of the analysis.

GR-strip (Geography-Referenced stripmap) imaging mode is a new imaging mode of SAR (Synthetic Aperture Radar), it is very different from the normal stripmap imaging mode. Operating in this mode, trajectory of aircraft can be un-parallel to swath, thus a higher flexibility is obtained due to the reduces of limitation for radar trajectory. Meanwhile, the characteristic of unlimited swath in azimuth orientation is also reserved in this mode. However, the shortest slant ranges are variant in GR-strip SAR for different points along azimuth direction on the strip and the resolution in azimuth is space-variance correspondingly. This cannot be solved by conventional SAR imaging algorithms. This article focused on two parts, the first part is the introduction of GR-strip SAR, the second part is a modified back-projection algorithm which is applicative to GR-strip SAR.

High resolution imaging is an important trend for SAR. The wide bandwidth signal is a must for high resolution. The error of traditional model based on the narrow bandwidth system may be inevitable for high resolution imaging. In this paper, a general SAR echo model is derived for the wide bandwidth system, and we also get the error factor between the traditional and general model. By simulating the two models, the conclusion is derived that if the transmitted signal is LFM waveform, the target is single and the receiving antenna is unit weight, the error of the two models can be neglected. The general echo model in more complicated situations is in the ongoing research. This study will make for the further research of high resolution SAR imaging algorithm.

This paper analyzes an innovative geographically referenced (GR) stripmap mode for the airborne synthetic aperture radar (SAR). In the GR stripmap SAR, the antenna beam illuminates orthogonally with the ground strip, which is not parallel to the SAR trajectory. Benefiting from the GR stripmap mode, the effective observation swath can be enlarged comparing to what can be realized by the traditional stripmap SAR. A modified nonlinear chirp scaling (MNLCS) method is suggested for the GR stripmap SAR imaging. It can solve the problem caused by scatterers' non-uniform Doppler history that disables most traditional imaging algorithms for the GR stripmap SAR. The MNLCS algorithm consists of three main steps. First, a bulk range migration compensation procedure eliminates the linear range migration (range walk). Second, an azimuth perturbation filter unifies the Doppler frequency modulation rate for each range cell. Lastly, a modified chirp scaling processing finishes the data focusing. Performance of the MNLCS algorithm was analyzed, followed by a series of computer simulation results, which validated the MNLCS for the GR stripmap SAR imaging.

A novel framework is proposed for mitigating azimuth ambiguities in spaceborne stripmap synthetic aperture radar (SAR) images. The azimuth ambiguities in SAR images are localized by using a local mean SAR image, SAR system parameters, and a defined metric derived from azimuth antenna pattern. The defined metric helps isolate targets lying at locations of ambiguities. The mechanism for restoration of ambiguity regions is selected on the basis of size of ambiguity regions. A compressive imaging technique is employed to restore isolated ambiguity regions (smaller regions of interconnected pixels), whereas clustered regions (relatively bigger regions of interconnected pixels) are filled by using exemplar-based inpainting. The simulation results on a real TerraSAR-X data set demonstrated that the proposed scheme can effectively remove azimuth ambiguities and enhance SAR image quality.

The polar format algorithm (PFA) is a wavenumber domain imaging method for spotlight synthetic aperture radar (SAR). The classic fixed-parameter PFA employs interpolation technique for data correction. However, such an operation will induce heavy computational load and cause degradation in computation precision. To optimize image formation processing performance, this study presents a novel parameter-adjusting PFA, which can implement SAR image formation at an extremely highly squint angle with obviously improved computation efficiency and imaging precision. In the parameter-adjusting PFA, radar parameters, such as center frequency, chirp rate, pulse duration, sampling rate, and pulse repeat frequency (PRF), vary for each azimuth sampling position. Due to the parameter adjusting strategy, the echoed signal can be acquired directly in keystone format with uniformly distributed azimuth intervals. In this case, range interpolation, which is necessary in the fixed-parameter PFA to convert data from polar format to keystone format, can be eliminated. Chirp z-transform (CZT) can be employed to focus SAR data along the azimuth direction. Compared with truncated sinc-interpolation, CZT was found to perform better in inducing less phase and amplitude errors in data processing. When residual video phase (RVP) compensation was accomplished for dechirped signal, the processing steps of the parameter-adjusting PFA were simplified as azimuth CZTs and range inverse fast Fourier transforms (IFFT). Lastly, computer simulation of multiple point targets validated the presented approach.

Typical features in SAR image like the bright line cause by double scattering are important datum in building height extraction, especially for the extracting method form a single SAR imagery. Its accuracy will affect the extraction result. In this paper, against to the imprecise of double scattering model, we modified its geometrical relationship. By using this model, we proposed a method with the usage of pixel interpolation and image correlation to extract the height of rectangular and cylindrical buildings.

The interpolation-based Polar Format Algorithm (PFA) can be used in bistatic Synthetic Aperture Radar (SAR) image processing while suffering from heavy interpolation computation load and complicated space-dependent resolution. To decrease computation load, this paper presents a nonlinear trajectory-based PFA, in which sensors are designed to fly on conical surface to avoid range interpolation. Due to this conical bistatic geometry, two advantages can be achieved. First, part of computation load can be converted to navigation system and processing speed can be improved. Second, a space-independent range resolution can be approached. A following multi-scatter simulation validates the presented approach.

This paper analyzes the synchronization of a bistatic synthetic aperture radar (SAR) system based upon a geostationary transmitter and small "receive-only" SAR systems mounted on airplanes or unmanned aerial vehicles (UAVs), namely GEO Spaceborne-Airborne Bistatic SAR. This paper will analyze and present our solution of the space synchronization, time synchronization, frequency synchronization and phase synchronization for the GEO Spaceborne-Airborne bistatic SAR respectively. It will first introduce the GEO Spaceborne-Airborne bistatic SAR system and geometry model briefly. Then possible solutions of the system synchronization will be discussed. It will introduce the synchronization scheme based on the direct path between the transmitter and receiver. It uses synchronization modules to realize the time and frequency synchronization. A mathematical model considering the time and frequency synchronization error is given and a phase error analysis is performed. Range and azimuth compressions of a single target are implemented by simulations.

The GMTI (Ground Moving Target Indication) performance for circular scanning SAR (Synthetic Aperture Radar) equipped on slow platform was analyzed in this paper. The characteristics of SAR equipped on slow platform and the GMTI application were descript in the beginning, and the circular scanning mode was advanced to resolve the low azimuth imaging efficiency of the slow platform borne SAR. Then the range models of the stationary and moving targets based on circular scanning mode were built, and the Doppler frequency expressions were derived from the range models. The detectable velocities distribution areas were derived by separating the Doppler frequency of two kinds of targets. The quantitative computer simulations were given to demonstrate the detectable velocities area along with azimuth angle and pulse repetition frequency in the end.