Sidelobe suppression is of particular importance in the SAR image quality improvement. However, the range and azimuth sidelobes are coupled and non-orthogonal in squinted SAR images, which makes traditional methods ineffective. This letter presents a sidelobe suppression method for squinted SAR complex data based on the SAR convolution model and minimum image sharpness. First, the convolution model of SAR images is revised with the subpixel offset. Then, the sidelobe suppression is achieved by deconvolution pixel by pixel. Innovatively, a convex optimization based on minimum image sharpness is built and solved to estimate the unknown and variant subpixel offset of each target. In addition, a new factor based on integrated side lobe ratio (ISLR) is applied for efficiency improvement. Finally, results on the squinted spaceborne SAR real data verify the effectiveness of the proposed method both in sidelobe suppression and the maintenance of amplitude-phase characteristics.

In Synthetic Aperture Radar (SAR) systems, accurate jamming recognition is essential for effective anti-jamming methods. With the rapid development and rich diversity of jamming techniques, compound jamming types has become particularly prominent. However, most existing deep learning-based recognition methods still rely on multi-classification for individual jamming types and sequentially process the signal in a single pulse repetition time (PRT). To overcome these limitations, this letter proposes a novel ML-SwinT-LSTM model for SAR compound jamming sequence recognition, which integrates Swin Transformer (SwinT), Long Short Term Memory (LSTM), and a multi-label classification head (ML head). Specifically, time-frequency (TF) spectrogram sequences, obtained from jamming signals, are fed into the proposed model to extract image features, capture sequence correlation, and independently identify the presence of each jamming component. The sequence length 20 is selected to ensure a trade-off between temporal information and computational cost. The effectiveness of the proposed model is validated using a simulated compound jamming sequence dataset for training and real jamming data for testing. Ablation and comparative experiments demonstrate that the proposed model achieves higher accuracy and lower computational complexity.

Deep learning has demonstrated significant potential in remote sensing scene classification; nevertheless, its efficacy is frequently hindered by the requirement for extensive labeled datasets and its restricted generalisation in practical applications. We developed a Self Supervised multimodal representation Learning (MMRL) framework for Local Climate Zone Classification (LCZC) to tackle these problems. Our approach utilises novel encoder architecture that derives representations exclusively from synthetic aperture radar (SAR) and multispectral (MS) data. To enhance the learning process, we implemented multimode loss and consistency loss, enhancing the model ability to prioritize the most meaningful features. The encoder, trained on the unlabelled So2Sat-LCZ42 dataset, acquires robust and transferable representations, further refined for a few-shot LCZ classification task utilising a restricted number of labelled samples. This approach allows the model to efficiently utilize extensive unlabeled data, leading to enhanced performance in subsequent classification tasks. Experimental findings on the So2Sat-LCZ42 benchmark validate that our self supervised learning (SSL) based approach achieved better accuracy than the current state-of-the-art(SOTA) SSL models, illustrating its efficacy for label-efficient LCZC.

Effective noise suppression is crucial for the subsequent interpretation tasks of SAR imagery. Traditional SAR image processing techniques often overlook the coherent nature of noise, leading to a loss of vital detail during filtering. With advancements in deep-learning, significant strides have been made in image processing. However, existing deep-learning methods do not fully leverage the imaging mechanisms of SAR, resulting in a lack of specificity and interpretability in the filtering process. To balance noise reduction with detail preservation and to address the “black box” issue in filtering, we propose an interpretable filtering method that employs a correlation-based upward search for density peaks. Initially, we develop an MeanShift-Markov Random Fields filter (MS-MRF) that integrates MeanShift with Markov Random Fields (MRF) in the joint spatial-spectral domain, ensuring both correlation and detail preservation; the derivation of the MS-MRF filter is rigorously grounded in mathematical theory. Subsequently, we integrate MS-MRF with convolutional operations in deep-learning to create a novel convolutional filter, Interpretable MS-MRF Convolution (IMMC), which enhances the model’s interpretability, noise reduction capabilities, and detail retention. Extensive experiments demonstrate that our method outperforms State of the art(SOTA) SAR denoising techniques, achieving an average SSIM of over 85.00% and an average PSNR exceeding 35.00dB across synthetic datasets with varying noise levels, showing significant improvements in noise suppression, detail preservation, and interpretability.

Signum Coded Synthetic Aperture Radar (SC-SAR), also known as the one-bit SAR, significantly reduces the system complexity and data processing throughput by retaining only the sign information of the echo signal. However, this introduces severe quantization distortion, which degrades the image signal-to-noise ratio (SNR) and generates a succession of false targets in sparse scenes. The theoretical analysis reveals that the SNR degradation is primarily caused by the phase harmonic distortion, while the false targets are brought about by the amplitude intermodulation distortion. To address these issues, a nonlinear frequency-modulated (NLFM) signal with a Hamming-windowed amplitude spectrum, termed Hamming Frequency Modulation (HFM), is proposed as the transmitted waveform. The matched filter for the HFM signal integrates the pulse compression and the low-sidelobe weighting, optimizing the output peak signal-to-noise ratio (PSNR) to suppress the quantization noise. Concurrently, its nonlinear frequency modulation disrupts the periodicity of the intermodulation distortion, attenuating the peak power of false targets. Experiments utilizing both ideal point targets and real SAR raw data demonstrate that the proposed HFM method suppresses the quantization noise and the false target energy by approximately 0.8 dB and 4.8 dB, respectively, compared to the conventional Linear Frequency Modulation (LFM) signal, thereby enhancing SC-SAR image quality significantly.

Scene matching involves establishing correspondences between multiple images of the same location and poses significant challenges for synthetic aperture radar (SAR) images due to the anisotropic scattering prosperities of SAR targets; variations in looking and azimuth angles further complicate the matching process. A matching algorithm is proposed based on a coarse-to-fine framework to address these issues. First, a coarse matching employing normalized cross correlation (NCC) with a sliding window is applied to filter out irrelevant regions, reducing distractions, and shortening the processing time. Subsequently, a Siamese neural network (SNN), incorporating ResNet-50 and convolutional block attention module (CBAM) for enhanced feature extraction, is introduced to learn and discern differences between inputs. The effectiveness and robustness of the proposed method are validated through extensive experiments using a self-made dataset derived from Umbra Satellite.

The advent of high-temporal technology enables spaceborne synthetic aperture radar (SAR) to capture sequential images at a high-frame-rate. By leveraging the extensive temporal information of these images, it becomes feasible to detect high-speed targets. This paper introduces a novel method for extracting the trajectories of high-speed weak targets using the kernel functions and the Hough transform. The proposed method capitalizes on the regular perturbations observed in high-speed targets within sequential images to extract trajectories effectively. Simulation experiments conducted to assess the performance of this method demonstrate its ability to accurately trace high-speed target movements, even under low signal-to-noise ratio (SNR) conditions.

Deep learning-based SAR image interpretation have gained much attention in recent years, in this paper supervised learning for SAR images is discussed first and then novel self supervised contrastive learning method is presented to reduce dependency on large amount of labeled data. SAR images provide valuable information about earth surface. Unlike optical images, SAR images are formed by backscattered signals influenced by surface roughness and other physical properties. Deep learning models can extract details from SAR data that may be missed by traditional image processing. Furthermore, deep learning models can learn patterens and features from SAR images to enable automated interpretation and analysis. It is difficult and time consuming to get labeled data. Supervised CNN is used to extract meaningful data from the SAR images for classification but it uses labeled data extensively, using self-supervised contrastive learning, positive pairs are constructed for each image; positive pairs are generated using augmentation of the SAR image. The contrastive loss function is designed such that it encourages the network to learn similar representation for positive pairs. A novel features extractor is introduced to get robust features. Contrastive loss and backpropagation are used to train the network without labels, after training, fine tuning is done on small number of labels called few shot. Downstream tasks such as classification is performed after fine tuning. Results are evaluated on the widely used SAR bench mark dataset.

Ship target detection based on SAR images is an important means of marine observation. Traditional target detection requires the most processing time to image the SAR echo. Considering the sparse distribution of ship targets in wide-swath marine SAR images, imaging and detecting processes on non-target regions seriously reduce efficiency. This paper proposes an integrated framework to improve marine SAR imaging detection efficiency by adding two steps of selection for target areas. Firstly, an RC-TextCNN network is designed to select target areas on azimuth direction from SAR echo one-dimensional compression data. After imaging selected areas, a dynamic quantization and threshold segmentation method is used to further remove non-target areas. Finally, suspected target areas are introduced into the pruned yolov7 model for final target detection. This workflow significantly minimizes computational and time costs. The experiment on Gaofen3 data shows that the speed of the process is increased by three times while detection accuracy is at 90%.

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.

Synthetic aperture radar (SAR) working at the terrain observation with progressive scan (TOPS) mode can obtain wide-coverage images. The improvement of image coverage makes further demands for higher radiation accuracy. However, traditional imaging algorithms do not take into account the effects of errors introduced by beam rotation, resulting in poor radiometric deterioration and distortion of image quality. This letter focuses on the specific manifestations of this phenomenon and the reasons for its formation. To improve the TOPS SAR image quality, a method based on the generalized cross correlation (GCC) of subsignals is presented to estimate the parameters of beam rotation in this letter. Experimental results with real spaceborne TOPS SAR data are provided to validate the analysis of this phenomenon and show that the proposed method improves the radiation accuracy for more than 0.2 dB.

Traditionally, synthetic aperture radar (SAR)-based ship target detection is performed in the image domain, where SAR imaging processing has to be applied first. However, SAR imaging processing is complex and time-consuming, especially in the wide-swath working mode. Actually, for open sea scenes, most echoes are sea surface signals with no ship targets, and there is no need for imaging processing in those areas. Therefore, non-image domain ship target detection is studied in this letter, and a novel Incept-text convolutional neural network (TextCNN) model is proposed for ship target detection in the SAR range-compressed domain (RCD). In the proposed method, the SAR echo data are converted into a 1-D range profile signal first by range compression and mean pooling, and then, the Incept-TextCNN model is proposed and applied, and information about existence of ship targets in relevant range cells will be its output. Finally, the effectiveness and efficiency of the proposed method is testified by simulation and real spaceborne SAR data, and the results demonstrate that the proposed model can filter out the invalid range-compressed data of the sea surface area, which can significantly reduce the amount of data for subsequent SAR imaging and ship classification.

In this study, we introduce a novel self-supervised learning adapter based on permutated image restoration (PIR) for effectively transferring pretrained weights from natural images to remote sensing object detection tasks. The adapter’s unique methodology encompasses a three-phase process: segmenting and permuting image blocks, estimating permutation matrices for sequence reconstruction, and applying specialized loss functions for accurate block positioning. The use of our approach results in the maintenance of fidelity in both absolute and relative block positions as demonstrated by the evaluation of block similarities. The empirical results indicate significant performance enhancements for diverse datasets spanning optical and SAR data types, including HRSC2016, SODA-A, and RSDD, while effectively avoiding overfitting.

Rotating ships usually suffer from complicated defocusing in high-resolution spaceborne synthetic aperture radar (SAR) imagery due to the non-uniform motions, which severely restricts the classification and interpretation of ships in SAR applications. In this paper, a novel refocusing method is proposed to improve the imaging quality of rotating ships by postprocessing SAR imagery. We first attempt to utilize the nonlinear chirp scaling (NLCS) principle to correct space-variant phase errors induced by target rotations. Specifically, an azimuth inverse compression approach without zero-padding operation is designed to recover the azimuth frequency modulation (FM) of the target image data. Then, a modified NLCS operation is derived for space-variant phase error correction by introducing a high-order perturbation function. An objective function based on image quality is also constructed to estimate the optimal perturbation parameters. Finally, simulated data and real spaceborne SAR data are processed to demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.

Processing high-squinted spaceborne SAR data in the sliding-spotlight mode is a challenging task due to azimuth spectral aliasing and range-azimuth coupling for frequency-domain imaging algorithms, and most critically, the variation of Doppler parameters causes significant reduction in the depth-of- azimuth-focus (DOAF). In this paper, a novel imaging algorithm is proposed for focusing high-squinted spaceborne SAR data in the sliding-spotlight mode. First, linear range walk correction (LRWC) and range frequency-dependent de-rotation are applied to remove the coupling of range frequency with the Doppler parameters. Then, a modified range migration algorithm (RMA) is derived for accurate focusing. The de-ramp operation combined with improved nonlinear chirp scaling (INCS) is employed for solving the aliasing problem of azimuth time and extending the depth-of-azimuth-focus in the third step. Finally, geometry distortion caused by LRWC is corrected. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

The ScanSAR mode can change the antenna angle during operation and obtain a wide swath by scanning multiple strips at one time. However, due to discontinuous working in azimuth, the scalloping effect in ScanSAR will degrade the image quality. In this paper, a novel adaptive scalloping suppression method is proposed by analyzing the complex scene as well as the scalloping distribution. First, the images of the various types of scenes are pre-processed so that the distribution of sub-images satisfies the Kalman filter conditions. Then, the problem of space-variant property is solved by performing adaptive blocking in the range direction. Finally, the Kalman filtering algorithm is introduced to process the scalloping in each sub-block separately, and the processed sub-blocks are fused to obtain the final result. The proposed method is verified by the real ScanSAR images of GF-3. Experimental results show that the proposed method is more efficient for scalloping suppression than the existing ones for both general and complex scenes, and has clear improvement for large-scale images with strong scalloping, which fully verifies the robustness and adaptability of the proposed method.

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.

High-resolution or Medium Earth Orbit (MEO) spaceborne synthetic aperture radar (SAR) system face the problem of long synthetic aperture time. In this case, ship targets with complex angular oscillation motions introduced by sea waves are blurred severely in SAR images. An effective way to refocus targets with rotation movements is inverse synthetic aperture radar (ISAR) technique. However, residual space-variant motion errors after ISAR processing still cause defocusing, especially in heavy sea states. In this paper, a coarse-to-fine refocusing method for moving ship targets is proposed. Firstly, a coarse refocused image is obtained via ISAR technique. Then, a novel space-variant and high-order motion compensation approach is devised to realize fine refocusing. Experiments on real spaceborne SAR data verify the effectiveness of the proposed method.

Sidelobe suppression is a challenging but crucial issue for SAR image quality enhancement. Inspired by the blind image restoration method based on the approximate greatest common divisor (AGCD) in natural images, a novel SAR sidelobe suppression method is proposed in this paper. Sidelobe suppression is interpreted as image deconvolution with a known PSF. Another SAR image with different PSF is generated by injecting the phase error at first. Then the PSF estimation is introduced into SAR, which is realized by solving the AGCD of two noised polynomials associated with two SAR images. Finally, the sidelobe suppression reduces to the solution of the inverse problem. Experiments on a TerraSAR-X SAR image show that improved results are qualitatively realized in sidelobe suppression and detail preservation in comparison with two existing algorithms.

Operating multi-channel synthetic aperture radar (SAR) in ScanSAR mode enables an ultrawide-swath and high-resolution imagery. A critical step in multi-channel SAR data processing is to accurately estimate and compensate phase inconsistency between channels. However, the Doppler spectrum distribution of ScanSAR relies heavily on the characteristic of illuminated regions, which may make the traditional time-domain phase estimation method failure under nonuniform scenes. In order to ensure that the performance of phase estimation is robust with respect to the variation of scenes, an innovative scene adaptive phase inconsistency estimation method for the ScanSAR system is presented in this letter, which can estimate channel phase error more accurately by extracting scene-center frequency from received data. The performance of the presented approach is evaluated using real airborne multi-channel SAR data. Experimental results show that the modified phase inconsistency estimation method is more effective and adaptable to different scenes compared with the conventional method.

In this letter, a moving vehicle detection method is proposed based on spaceborne synthetic aperture radar images, generated by antenna azimuth steering with different azimuth angles and time lags. First, the multisquint geometry and sequential image model are established, and the variation from stationary scatters over sequence is addressed, which interferes the indication of moving signals significantly. Then, the robust principal component analysis (RPCA) is employed in the image domain as the predetection step. Next, with some spatial filters and thresholding estimators, the moving target signature is exploited to further discriminate the residual false detection. Finally, the proposed method is verified by both simulated and real images based on TerraSAR-X data. Results reveal that the clutter suppression can be effectively achieved.

Polarimetric synthetic aperture radar (PolSAR) image semantic segmentation is currently of great importance for synthetic aperture radar (SAR) image interpretation, especially in agricultural applications. Several convolutional neural networks (CNNs) have been implemented for SAR image semantic segmentation in urban or land cover applications. However, existing CNNs often break one semantic area into several pieces or confuse the adjacent semantic area in a PolSAR image. To address these issues uniformly, a refined pyramid scene parsing network (PSPNet) is proposed for PolSAR image semantic segmentation in an agricultural areas. Compared to conventional PSPNet architecture, the refined PSPNet adopts a multilevel feature fusion design in its decoder to effectively exploit the features learned from its different encoder branches. Besides, a polarimetric channel attention module is incorporated into the network to capture rich polarimetric features in a PolSAR image. Furthermore, an edge-aware loss function is devised to guide the network to refine pixel-level edge information directly from semantic segmentation prediction, separating easily confused agriculture area with sharp contours. Experimental results on one airborne millimeter-wave PolSAR dataset verify that the proposed network achieves promising semantic segmentation accuracy and preferable spatial consistency.

For azimuth multi-channel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets and image defocusing, etc. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness. First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing normalized image sharpness, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of objective function. Finally, the ultimate image is formed through adding up phase compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method.

The recent local climate zones (LCZ) classification scheme provides spatially fine granular descriptions of inner urban morphology. It is universally applicable to cities worldwide and capable of supporting various urban studies. Although optical and dual-pol synthetic aperture radar (SAR) data continue to push the frontiers of this task, the potential of quad-pol SAR data for LCZ classification is not yet explored. In this article, we propose a novel complex-valued convolutional neural network (CNN),SAR4LCZ-Net, to tackle this challenge. SAR4LCZ-Net improves the state-of-the-art by exploiting two facts of this specific task: the semantic hierarchical structure of the LCZ classification scheme and the complex-valued nature of quad-pol SAR data. To validate the performance of our algorithm, we generate a Chinese Gaofen-3 quad-pol SAR dataset for LCZ which covers 31 cities around the world. Results show that the proposed SAR4LCZ-Net improves 2.4% on overall accuracy (OA) and 4.5% on average accuracy (AA) compared with the real-valued CNN with the same structure. Gaofen-3 quad-pol SAR data also showed its advantage over the dual-pol Sentinel-1 data. It enhanced 5.0% on OA and 7.2% on AA in LCZ classification, under a fair comparison with a model trained by Sentinel-1 of the same area.

From the multisquint spaceborne Synthetic Aperture Radar (SAR) images, a novel method with low computing cost is proposed to perform the sea-land coarse discrimination. Both the two and three-term Low Rank and Sparse (LRS) decompositions are used, where the sparse and error terms are profited, respectively. The differences between each image pairs are then stacked and averaged. A thresholding test is finally operated. The experiment based on the real TerraSAR-X data preliminarily reveals that the land and sea areas could be roughly separated. The application includes avoiding territorial false alarms when conducting maritime surveillance in the sea-land junction region.

The azimuth multichannel technique has been widely used in synthetic aperture radar (SAR) systems for improving the resolution and expanding the illumination area. However, due to phase inconsistency (PI) of different channels, the image quality deteriorates significantly, including resolution loss and appearance of ghost targets. In this letter, by exploiting the rotation-invariant property of the steering vector of the multichannel SAR signal, a PI error compensation method is proposed based on the estimation of signal parameters by rotation invariance technique (ESPRIT). Experimental results are presented using both simulated and real data to demonstrate the performance of the proposed method.

Scanning synthetic aperture radar (ScanSAR) mode is widely used in Earth observation because of its capability of acquiring wide-swath images with moderate resolution. However, due to the operation mechanism of ScanSAR mode, the acquired images often suffer from the scalloping problem, resulting in significant deterioration of image quality. In this article, a novel scalloping suppression method is proposed for ScanSAR images based on the modified Kalman filter with preprocessing. First, an image model is built to analyze the effect caused by scalloping. Then, a modified Kalman filter is proposed to estimate the intensity of scalloping. However, if the scene is complicated or the scalloping effect is strong, the Kalman filter works with poor performance. Therefore, an innovative preprocessing operation is introduced, involving image segmentation and pixel value filling. Finally, the proposed method is verified by the GaoFen-3 (GF-3) and TerraSAR-X satellite images with different scenes. The results demonstrate that the proposed method can accommodate the complex scene well and achieve effective scalloping suppression.

With the development and extensive utility of synthetic aperture radar(SAR), SAR systems are required not only to achieve the large-scale imaging but also to continuously stare at focus areas. Accordingly, SAR based on highly elliptical orbit provides a new way for solving these observation problems. This paper presents a method for spaceborne SAR echo simulation in a highly elliptical orbit based on the antenna beam steering control. Due to the effects of the high eccentricity and the apogee of the highly elliptical orbit, squint angle, looking-down angles of antenna beam and other factors are considered to design the beam control of the antenna, so that the satellite can keep illuminating on the earth surface to acquire echo for the whole cycle. In this paper, the proposed method of antenna beam steering implements the SAR echo simulation in highly elliptical orbit. The feasibility of the proposed method was analyzed by the steering control angles' variation characteristics and verified by simulating the lattice targets echo signal and imaging.

High-resolution spaceborne P-band SAR has broad prospects in the future due to its penetrating capabilities. However, conventional imaging methods are no longer suitable for high-resolution P-band SAR because of ionospheric effects including dispersion and scintillation. In this paper, an imaging compensation scheme with modified Chirp Scaling (CS) has been proposed, correcting ionospheric effects by a new spectrum segmentation method and Phase Gradient Autofocus (PGA). The effectiveness of the proposed compensation scheme for high-resolution P-band SAR has been verified by point target and scene simulation with ALOS-PALSAR data.

With the video synthetic aperture radar (ViSAR) system proposed, it becomes possible to generate synthetic aperture radar (SAR) images with high frame rate. Benefiting from high frame rate, pixel intensity in a SAR image sequence changes approximately continuously, providing a new way to detect targets in time domain. This paper presents a weak moving point target detection method based on high frame rate SAR image sequences and machine learning. In our method, statistical features are extracted from time domain for distinguishing between background and target. At the same time, the detection problem is transformed into a binary classification problem of determining whether the target exists based on machine learning and these statistical features. The experiments are evaluated using simulated SAR data and the results show that the proposed method can be used to detect moving point targets at a low signal to noise ratio (SNR).

Azimuth multi-channel technology has been widely used in high-resolution wide-swath synthetic aperture radar (SAR). However, for airborne multi-channel SAR, motion error decreases image quality seriously, including resolution loss and false target appearance. In this paper, a multi-channel SAR imaging method is proposed combined with improved two-step motion compensation. First, multi-channel SAR motion error geometric model is established and the range-invariant channel-dependent phase error is derived. After that the range-dependent phase error for different channels is analyzed in detail. Then, the multi-channel imaging method with motion compensation is addressed. Finally, airborne SAR data is used to validate the proposed method. Processing results show that the proposed method can correct motion error and improve image quality effectively.

ScanSAR images are widely used in both military and civil fields with the capability of wide swath. However, the scalloping effect seriously affects the quality of scanSAR images, especially in the complex scenes, e.g. the sea-land junction scene. This paper presents a modified Kalman-filter method for scalloping suppression. First, the scanSAR image model is built, considering the scalloping effect and noise. Then, Kalman filter is adopted for suppressing the scalloping effect. Moreover, pre-processing method, on the basis of image statistical characteristics, is implemented to accommodate complex scene. Specifically, the pre-processing, involving image segmentation and brightness filling, divides the image into sub-images with different brightness and provides linear-Gaussian condition for Kalman filter through brightness filling. Finally, the method is verified by GaoFen-3(GF-3) SAR images, with the discussion and conclusion.

Based on the multiple azimuth squint angles mode, a novel moving target velocity estimation method is proposed, including both along range and along azimuth directions. The acquisition geometry of multi-azimuth angle mode is given first with variation of antenna azimuth squint angles. Then, range curvature is corrected in range-frequency domain after range compression and the residual range curvature can be neglected which is smaller than a range bin. Furthermore, azimuth velocity is estimated by the slope variation of the range walk trajectory from two certain observations and then is the range velocity estimation based on the slope from one observation. Velocity estimation accuracy is also analyzed, considering the errors caused by azimuth squint angle and trajectory slope. The effectiveness of the proposed strategy is demonstrated by experimental results.

This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.

Global navigation satellite system (GNSS)-based bistatic radar has become an attractive technology in remote sensing applications, and coherent integration is a vital operation for improving its detection ability due to the very weak signal. For small to medium UAV detection, one problem is the coherent integration loss due to issues such as the Doppler-intolerant characteristic of GNSS signal and the large range cell migration over long coherent integration time. In this paper, a modified UAV detection algorithm is proposed. The quadratic phase term caused by transmitter motion is first compensated, then the UAV target echo signal is focused in the Range-Doppler domain by an improved Radon Fourier Transform (RFT) with range-walk removal and Chirp-Z transform. Finally, a refined range matched filter with a shifting Doppler is applied for range compression, and the UAV target is accurately focused in the range-Doppler plane. Numerical simulations demonstrate that the range and Doppler parameters of UAVs can be effectively obtained.

Synthetic Aperture Radar (SAR) is widely used in remote sensing field, especially for the acquisition of quantitative information about the earth’s environment. Calibration processing is critical for SAR image quantitative applications. This paper proposes a spaceborne SAR calibration simulator to describe the calibration process. First, Amazon rainforest area SAR image from Gaofen-3 was chosen to generate backscatter coefficient by adding a reference reflector for the purpose of quantitative analysis. Then the backscatter coefficient was entered into the spaceborne SAR calibration simulator. The simulator will perform echo simulation based on the system parameters of Gaofen-3. Based on the simulation echo, image formation processing is implemented for generating the focusing image. Furthermore, peak method and integral method were utilized to calibrate the SAR image. Simulation results show that integral method is better for calibration accuracy.

Orbital stations can be an alternative platform choice for SAR payloads besides professional remote sensing satellite platform. However, such platform cannot provide as precision motion parameters as remote sensing satellite platform for SAR imaging due to its unique structure. The influence of motion parameter errors on SAR azimuth resolution degradation should be taken into consideration. Selecting the second order phase error caused by Doppler frequency rate with error as intermediate variable between motion parameter errors and SAR azimuth resolution, this paper proposed a Monte Carlo simulation model has been set up to obtain the specific probability distribution of SAR azimuth resolution degradation. A typical Monte Carlo simulation result is given to show the effectiveness of the method and may help actual SAR payload system design.

Quantization step size is a very important system parameter in TOPS and sliding spotlight acquisition modes, as the azimuth antenna beam is electronically steering during the illumination. By replacing continuous steering, staircase-like steering in azimuth antenna beam is often used in orbited SAR, causing a quantized error on azimuth antenna pattern (AAP) and an amplitude modulation on illuminated target echo. Consequently, infinitely many spurious targets are introduced in azimuth direction. In this paper, investigations of quantized AAP on TOPS and sliding spotlight SAR image are carried out, by unified modelling on quantized AAP and spurious targets signal. Then, the position of maximum peak of spurious targets and its peak level are derived, which is only decided by azimuth antenna length, signal wavelength and quantization step size, and have nothing to do with the acquisition mode. Finally, simulation results verify the correctness of the proposed mathematical model of spurious target.

The vertical electron density distribution can have a significant impact on ionospheric measurements of Total Electron Content (TEC) derived from estimates of Faraday rotation (FR) with spaceborne low-frequency SAR data. Using vertical electron density data and the IGRF-12 magnetic field model, simulations of FR under different vertical electron density distributions for an L-band SAR have been performed. The usual approximation that the geometric and magnetic field parameters can be estimated at 400 km when converting FR to TEC is shown to cause significant errors, especially for regions at low latitude and near the zero FR line.

The multi-azimuth-angle (MAA) UAV SAR is introduced in this paper. Compared with the traditional TomoSAR that rebuilds the 3-D scene only from the boresight aspect, the MAA UAV SAR can rebuild the 3-D scene from different multi azimuth angles and then provide multiple or even full aspects 3-D image by the novel 3-D image formation algorithm presented in this paper. Firstly, the MAA UAV SAR is introduced, followed by the 2-D imaging algorithm to focus the raw data. Then, a novel squinted tomography method is provided to obtain squinted 3-D image using multiple 2-D images with the same azimuth angle. Finally, the squinted 3-D images are geolocated and fused to construct a full 3-D image. Imaging results are provided to demonstrate the better performance of the MAA SAR.

Comparing with the traditional global navigation satellite system (GNSS) based Bistatic SAR (GNSS-based BiSAR), the BiSAR based on GPS-L5 signal plays a superior role in remote sensing applications, as for its feature of stronger power and larger bandwidth. In applying this method, the consequent problems, yet, such as long dwell time and large range cell migration (RCM) are inevitably leading the traditional image processing algorithm non-applicable. Therefore, a refined imaging formation algorithm for this BiSAR data imagery was proposed. Firstly, a modified bulk RCM correction (RCMC) was presented based on a fifth-order two-dimensional point target spectrum. After bulk RCMC processing, the phase resulted from RCM and range-azimuth coupling at the reference range was removed. Then, as the bulk RCMC dramatically changed the echo's range history, a refined hybrid correlation operation emerged to compensate the residual phase errors. Finally, the simulation and imaging results measures the validity and accuracy of the proposed method.

In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images.

There are two challenges for efficient processing of both the sliding spotlight and terrain observation by progressive scans (TOPS) data using full-aperture algorithms. First, to overcome the Doppler spectrum aliasing, zero-padding is required for azimuth up sampling, increasing the computation burden; second, the azimuth deramp operation for avoiding synthetic aperture radar (SAR) image folding leads to azimuth time shift along the range dimension, and in turn the appearance of ghost targets and azimuth resolution reduction at the scene edge, especially in the wide-swath case. In this paper, a novel three-step algorithm is proposed for processing the sliding spotlight and TOPS data. In the first step, a modified derotation is derived in detail based on the chirp z-transform (CZT), avoiding zero-padding; then, the chirp scaling algorithm kernel is adopted for precise focusing in the second step; and in the third step, instead of the traditional range-independent deramp, a range-dependent deramp is applied to compensate for the time shift. Moreover, the SAR image geometry distortion caused by range-dependent deramp is corrected by employing a range-dependent CZT. Experimental results based on both simulated data and real data are provided to validate the proposed algorithm.

Aimed at the problems that SAR (Synthetic Aperture Radar) environment parameter inversion and data processing are lack of both overall design and synergy, this paper proposed a novel unification scheme for environmental remote sensing application. Compared to the reality that application follows data processing in the frontend, the performance of application is highlighted as the ultimate objective in the proposed scheme, where the frontend procedures should serve the backend application to get a better result. Frontend processing is divided into three parts: system design, imaging processing and post-processing procedure, while backend processing is categorized as image processing and environment parameter inversion. The concept of unification scheme focuses on the feedbacks between frontend and backend, and some demonstrations are also given in the following sections.

State of the art UAV technology shows its advantage in both civil and military applications, and it is required to meet the needs for payloads special designed for UAVs. However, electric powered multi-rotor UAV provides limited power and more sensitive to the payloads mass. Linear frequency modulated quasi continuous wave SAR leads to small and efficiency radar system which meets multi-rotor UAV's requirements. This paper provides a C-Band UAV SAR combining with linear modulated quasi continuous wave technique as well as integrated radar and communication design. Experiments will be taken to verify the usage of UAV SAR in typical SAR working mode and GMTI ability.

To process squinted sliding-spotlight synthetic aperture radar data, the azimuth preprocessing step based on the linear range walk correction (LRWC) and derotation operations is implemented to eliminate the effect of 2-D spectrum skew and azimuth spectral aliasing. However, two key issues arise from the azimuth preprocessing. First, the traditional chirp scaling (CS) kernel is not suitable for data focusing because the property of the 2-D spectrum is changed significantly; second, the spatial variation of the targets' Doppler rates along the azimuth direction due to the LRWC operation limits the depth-of-azimuth-focus (DOAF) seriously. In this letter, a modified accurate CS kernel is derived to realize range compensation. Then, an azimuth spatial variation removing method based on the principle of nonlinear CS is proposed to equalize the Doppler rates of the targets located at the same range cell, which can extend the DOAF and improve processing efficiency. Finally, a novel imaging algorithm is proposed, with its effectiveness demonstrated by simulation results.

Spaceborne squinted sliding spotlight synthetic aperture radar (SAR) can achieve high-resolution image with well flexibility. However, traditional imaging algorithm cannot focus its data, as the serious coupling between the range and the azimuth and the azimuth spectral aliasing problem. To process the data, a refined two-step algorithm is proposed in this paper. Firstly, the linear range cell migration correction (LRCMC) and De-rotation are adopted to solve the azimuth spectral aliasing problem. Then the data is focused with a modified wavenumber domain algorithm (WDA). Also, the azimuth nonlinear chirp scaling (NCS) method is used to solve the limited depth-of-azimuth-focus (DOAF) problem. At last, the geometric correction is adopted to correct the geometrical distortion. The computer simulation results verify the validity of the proposed imaging algorithm.

Motion compensation (MOCO) is the key step in airborne Synthetic Aperture Radar (SAR) imaging processing. Motion errors can be obtained from the navigation data which is acquired from the inertial navigation system (INS) mounted on the plane. However, the accuracy of the navigation data is limited which is far from application requirement for high resolution system. Accordingly, raw-data based MOCO approach is necessary in airborne SAR azimuth focusing which is defined as autofocus method. The autofocus method includes no-parametric techniques such as phase gradient autofocus (PGA) method and parametric techniques including contrast optimization algorithm (COA), map drift algorithm (MDA), etc. Based on the conventional two-step MOCO method, this paper proposes an improved two-step MOCO technique combining the parametric and the no-parametric techniques to alleviate the dependence on the navigation measurement. Firstly, an improved sub-aperture COA is implemented to acquire the Doppler rate accurately which can be utilized in the estimation of motion errors. In the next step, PGA is applied to eliminate the residual phase errors. Imaging results on real strip-mode airborne SAR data validate the proposed MOCO approach.

Spaceborne Synthetic Aperture Radar (SAR) has attracted special interests in many applications, but suffers from image quality degradation caused by azimuth ambiguities. To suppress this phenomenon, different from conventional suppression method, this paper concentrates on the analysis of precise mathematic expression of azimuth ambiguities. Exploiting the specific characteristic of ambiguities, a restoration algorithm to suppress azimuth ambiguities of stripmap SAR is proposed. This method is not sensitive to SNR, and also suitable for distributed targets. Azimuth ambiguities are accurately restored and then cancelled in SAR image. Proposed method has been shown effective by experiments, demonstrating capabilities of removing artifacts and revealing covered targets in stripmap SAR image.

Block adaptive quantization (BAQ) is widely utilized for reducing downlink data rate of spaceborne synthetic aperture radar (SAR). The mutual information upper bond of BAQ compressed data is proposed by means of establishing a mathematical mapping relationship between output signal entropy and input signal saturation degree, using information theoretical analysis method. Threshold saturation degree of input signal for BAQ to be effective are also obtained. Simulation SAR raw data are used to verify the correctness of the mapping relationship.

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.

Two challenges have been faced in signal processing of ultrahigh-resolution spaceborne synthetic aperture radar (SAR). The first challenge is constructing a precise range model, and the second one is to develop an efficient imaging algorithm since traditional algorithms fail to process ultrahigh-resolution spaceborne SAR data effectively. In this paper, a novel high-order imaging algorithm for high-resolution spaceborne SAR is presented. First, a modified equivalent squint range model (MESRM) is developed by introducing equivalent radar acceleration into the equivalent squint range model, and it is more suitable for high-resolution spaceborne SAR. The signal model based on the MESRM is also presented. Second, a novel high-order imaging algorithm is derived. The insufficient pulse-repetition frequency problem is solved by an improved subaperture method, and accurate focusing is achieved through an extended hybrid correlation algorithm. Simulations are performed to validate the presented algorithm.

For high resolution spaceborne SAR system, the errors caused by straight trajectory assumption and stop-go approximation cannot be ignored. In this paper, an algorithm is proposed to compensate the above errors. First, a novel modified hyperbolic range equation (MHRE) is introduced to describe the satellite's orbit which is more precise than the traditional hyperbolic range equation (THRE). Then, the accurate signal model without stop-go approximation is deduced based on the MHRE. What's more, the residual phase errors in two-dimensional frequency domain caused by the two error sources are obtained. At last, the accuracy and validity of the proposed compensation algorithm are verified by the computer simulation results.

Airborne synthetic aperture radar (SAR) systems are very sensible to deviations of the aircraft to the reference flight path. The trajectory errors can be divided into along-azimuth errors and the line-of-sight (LOS) displacement, while the latter one is the main source of motion errors. The LOS displacement can be obtained from the navigation data in which an ideal flight path should be assumed first. However, the accuracy of the assumed nominal trajectory is limited especially in high resolution SAR systems. To improve the precision of the estimation of LOS displacement, we present a novel method to acquire a precise flight path in the imaging intervals. This method can estimate trajectory deviation precisely by making full use of the navigation data.

In order to generate high accuracy data for space-borne Synthetic Aperture Radar (SAR) image interpretation, a novel parallel SAR echo generation approach is proposed in this paper. Target's backscattering coefficient is variable at the condition of wide bandwidth and large aspect angle. Considering targets' space-variant and time-variant backscattering, electromagnetic scattering at arbitrary aspect angle and frequency point is calculated accurately by Finite Difference Time Domain (FDTD). However, FDTD is a time-domain method causing large computation load, thus a parallel processing scheme is designed to reduce simulation time. It was observed that proposed approach provided higher image quality compared to classical method, and parallel scheme was also verified effective in the experiment.

To high resolution spaceborne SAR, one problem is inevitable that as to targets with elevation downs in one scenario, their imaging quality will decline after processing with imaging algorithm. SAR images are confined to two-dimensional domain and it's hard to reflect targets' height property. Instead, target A with some height is projected to target B with no height while A and B are in the different range door but with the same slant range from the satellite. Doppler parameters of B are used to image A due to the two-dimensional limitation and then azimuth defocusing and range location shift will occur. Target height can be regarded as one error source. In this paper, imaging quality of targets with height error is improved through self-focusing method among three-step focusing algorithm as to high resolution sliding spotlight SAR. Experiment results show that it's effective.

In highly-squint strip-map Synthetic Aperture Radar (SAR) onboard high-speed platform, the large range-walk is eliminated by continuously varying its pulse repetition interval (PRF), which overcomes the limitation of echo data collection. However, the accompanying problems of azimuth non-uniform sampling (ANS) and changing of Doppler history are inevitable. Therefore, an imaging formation algorithm for this SAR data imagery is proposed firstly. In the proposed algorithm, the range cell migration correction is performed to eliminate the changing of Doppler history. And the baseband Lagrange interpolation is implemented to reconstruct the ANS data. Then, the bulk compression function and stolt interpolation relationship are deduced in the focusing processing. Finally, the imaging results justify the validity and accuracy of the proposed algorithm.

Squinted multi-channel SAR is a available mode to achieve the high-resolution and wide-swath, because it is more flexible than broadside SAR. However, the traditional processing algorithm of broadside multi-channel mode is not suitable for squinted multi-channel mode, because the high squint angle will lead the reconstruction to fail. In this paper, a modified reconstruction method adapted to squinted multichannel SAR signal is proposed. The large central Doppler frequency of the squinted Doppler spectrum is taking into account, and the reconstruction method is modified accordingly. Meanwhile, the range walk correction is performed before the reconstruction to remove the mismatch between the reconstruction filters and the squinted signal with large range cell migration. Furthermore, the processing algorithm based on the wave-number algorithm framework with range walk correction is proposed. Finally, computer simulation results are presented to demonstrate the validity of the proposed algorithm.

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.

With the resolution of SAR developing continually, it is found that in the process of image processing, using equivalent squint range model would bring large phase error. So using traditional CS algorithm for processing will encounter difficulties. In order to solve this problem because of unsuitable slant range model, this paper introduces the fourth-order slant range model to replace the original model, and conducts a study on CS algorithm based on the fourth-order slant range model. This paper introduces the theoretical formulation and simulation results of the new algorithm. This algorithm can perform well for both low-resolution space-borne SAR and high-resolution space-borne SAR.

This paper deals with the problem of azimuth imaging worsening when three-step focusing algorithm fulfills imaging requirements of sliding spotlight SAR with wide azimuth swath. This is caused by spaceborne sliding spotlight SAR spatial variation characteristic: single point target's relatively large range cell migration and azimuth point targets' different Doppler experiences at different azimuth locations. To solve this problem phase error introduced by the equivalent strabismus model is compensated and the reference Doppler rate in the first step of TSFA is modified. A comparison of the imaging results to one azimuth 15km swath scene between refined TSFA and original TSFA is exhibited through simulations. The results prove that refined TSFA is able to get consistent good azimuth imaging quality of wide swath scene.

The squint synthetic aperture radar (SAR) is capable of improving the coverage performance of SAR system while suffering from large range cell migration (RCM) and heavy computation load. To alleviate RCM, this paper proposes an innovative sliding receive-window (SRW) technique, in which the receive-window starting time varies pulse by pulse as a function of range-walk. Moreover, a refined Omega-K algorithm is deduced for focusing highly squint airborne stripmap SAR using SRW technique. In refined Omega-K, a new stolt interpolation relationship is found for the RCM is modified by the SRW technique. Finally, the imaging results justify the validity and accuracy of the refined Omega-K algorithm.

The side-lobes of strong target in conventional SAR image always impact the image qualities, sometimes submerge the weak targets. A novel SAR scheme with ultra-low range PSLR performance was proposed, by employing CC-S as SAR transmitted waveform. The CC-S coding waveform was utilized to acquired ultra-low range PSLR performance, by mutually canceled the side-lobes. As the CC-S is composed of groups of self-complementary sequences, each sequences should be transmitted with proper scheme, and the receiving time difference of each sequences should be compensated. Furthermore, the image formation algorithm for accurately focusing raw data of the SAR system was also proposed. Computer simulation results were presented, which demonstrated the validity of the proposed SAR scheme and image formation algorithm.

Agile small SAR has broad applications, due to its high mobility and wide coverage. In this paper, we put forward an attitude steering strategy for agile small SAR satellite with sliding spotlight mode. This attitude steering strategy contains three steps. And according to the results of simulation, this attitude steering strategy has a very good performance.

MIMO radar can be considered as a particular communication system, which enjoys similar profits to the MIMO communication theory. Based on the information theory, the concept of channel capacity was introduced into the MIMO radar system. In this paper, the capacity expressions for MIMO SAR system were derived. As is shown in the simulation results, the channel capacity of the MIMO SAR depends on the number of the antennas, channel character and radar system parameters. The capacity of MIMO SAR increases with pulse repetition frequency and decreases with range and velocity. Furthermore, the larger the antenna number is, the more information can be obtained from the echoes. Meanwhile, the MIMO SAR capacity is larger than phase array SAR in high SNR.

This paper conducted thorough research to the theory of space-borne Sliding spotlight and TOPS data processing. First, the unified definition of hybrid factor of both TOPS and sliding spotlight mode was given. Then, the hybrid factor was analyzed and amended along the range direction combined the geometry model. Combined with amended hybrid factor, azimuthal time-frequency characteristic of was analyzed and discussed in detail by mathematic derivation. Based on the analysis, an efficient and precise extended three-step image formation algorithm was presented for sliding spotlight and TOPS image formation. Finally, the imaging results justify the effectiveness of the extended three-step algorithm.

Triple-Channel SAR system can detect moving target, and estimate its range velocity. However, the problems of blind velocity and velocity ambiguity still exit. To resolve these problems, Triple-Channel Multi-Input Multi-Output SAR (Triple-Channel MIMO-SAR) system, with a displaced phase center antenna (DPCA) and interferometry method based on matched Fourier Transform (MFT), is proposed in this paper, which could combine detection and estimation results of different working frequencies and obtain accurate Doppler frequency modulated rate estimation. Using this method, we can not only detect moving target and estimate its range velocity, but also resolve the problems of blind velocity and velocity ambiguity and get accurate azimuth velocity estimation. The effectiveness of this approach is validated by the computer simulation results.

Terahertz(THz)radiation, which occupies a relatively unexplored portion of the electromagnetic spectrum between mid-infrared and microwave bands, offers innovative sensing and imaging technologies that can provide information unavailable through conventional methods such as microwave and X-ray techniques. Spectroscopy in the terahertz frequency range has demonstrated unique identification of both pure and military-grade explosives. Explosive and illicit drug materials have characteristic THz spectra, a fuzzy neural network for classifying explosives and illicit drugs based on different THz spectra was designed. The designed Classifier can make hard decision and soft decision for identifying 9 patterns of explosives and illicit drugs at the accuracy of 89%, The highly successful result of the experiments, coupled with availability of practical THz systems which operate outside the laboratory environment, indicate that THz technologies are promising for the identification of explosives and illicit drugs.

Range migration of space borne SAR at large squint angle is much greater than the side-looking SAR, and longer echo receiving window is needed. Thus, the traditional beam position design method is invalid. In this paper, the method of drawing zebra map is improved by taking the range migration into consideration. The maximum and the minimum slant ranges during the synthetic time are derived. This paper also analyses the relation between the effective swath width and the range beam width at large squint angle. Simulation for X-SAR system proves that a given azimuth resolution limits the squint angle. STK and echo simulation are used to verify the validity of the improved beam position design method.

This paper conducted thorough research to the theory of space-borne TOPSAR data processing. The time-frequency characteristic of TOPSAR mode signal was analyzed and discussed in detail by mathematic derivation combined with the TOPSAR factor. Based on the analysis, an efficient and precise three-step image formation algorithm was presented for TOPSAR image formation without data division, and the operation in every step was theoretically proved effective. The de-rotation operation in the first step and deramp operation in the third step are adopted to finish the stretch in both frequency and time domains respectively, by which the azimuth folding effects in the time and frequency domains are overcome.