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.

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.

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%.

The Global Navigation Satellite System (GNSS), as an illumination source with broad coverage capabilities, is considered for passive radar target detection. Due to the low signal power, long-time integration is required for improving the energy of moving targets. However, the receivers often use independent local oscillators, and the bistatic configuration brings challenges to signal synchronization. This paper proposes a time-frequency synchronization method based on dual-channel processing for GNSS-based PR. Firstly, the direct channel data is used to construct the range delay and Doppler phase compensation factors. Then the time-frequency synchronization error in the reflect echo is eliminated using the compensation factors. Finally, direct wave suppression is performed for the subsequent energy integration and target detection. A ship detection experiment has been conducted using GPS L1 signal. The results show that the synchronization errors caused by satellite motion and receiver local oscillator drift are eliminated and direct wave is effectively suppressed. The container ship is successfully detected in the range Doppler (RD) domain.

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.

The global navigation satellite systems (GNSS), as illuminators of opportunity, form passive bistatic radar (PBR) systems that offer advantages, such as wide coverage, low power consumption, and low cost, making them suitable for moving target detection. However, the low power budget and small Doppler tolerance of GNSS signals pose challenges for energy accumulation and detection for moving targets. Additionally, range migration and Doppler shift caused by target motion during long-time integration must be addressed. This letter proposes a coarse-to-fine Doppler parameter search method for long-time coherent integration of maneuvering targets. In the coarse parameter search stage, the first-order Doppler phase modulation is removed within and between pulses. Pulse compression and range migration correction are accomplished using a unified range compensation filter. In the fine parameter search stage, quadratic Doppler phase modulation is compensated, and the fast Fourier transform (FFT) is used to refine the Doppler search step size efficiently. An experiment using GPS L1 signal as the illuminator is conducted, successfully detecting a ship target. The comparison results indicate that the proposed method significantly improves implementation efficiency without the loss of coherent integration gain.

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.

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.

Azimuth multichannel synthetic aperture radar(SAR) is a form of SAR with one transmitter and several receivers, which is able to obtain both high resolution and wide swath imaging. Azimuth spectrum reconstruction is core operation in the data processing of azimuth multichannel SAR. The spatial variation of Doppler center frequency is non-ignorable in multichannel SAR of squint view, meanwhile, in sliding spotlight SAR imaging mode, the Doppler spectrum of the signal increases greatly due to the rotation of the beam direction, thus, the traditional reconstruction algorithm fails to reconstruct the signal spectrum correctly. To solve above problems, an improved azimuth spectrum reconstruction and imaging processing algorithm for squint multichannel SAR in sliding spotlight mode is proposed. By analyzing the characteristics of multichannel signals in two-dimensional frequency domain, a new spectrum reconstruction matrix is established. Besides, by combining the Deramp operation with the reconstruction algorithm, the Doppler bandwidth caused by the rotation of the beam center is removed. Finally, imaging algorithm of single channel SAR is used to image the reconstructed signal. Simulation experiments verify the effectiveness of the method.

Global Navigation Satellite System (GNSS)-based Passive Radar (GPR) is gaining increasing attentions recently due to its potential on moving target detection (MTD). The main problem of the GPR is the low power density of GNSS signals. With the upgrades of GNSS, the total transmit power for the new launched satellites has been greatly improved. However, the total transmit power is allocated to several independent signals, which cannot be directly integrated. This paper proposes a multi-source image fusion method for the GPR which aims at coherently integrating the power allocated to different signals for the same satellite, and improving the detection performance. Validation experiments using GPS signals as illumination source and an airplane as target are conducted. The target is successfully detected by exploiting GPS L1 and L5 signals for the same satellite as illumination sources. Two-image fusion is performed utilizing the proposed method. Significant SNR improvement is achieved, which validates the feasibility of the proposed method.

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.

The Global Navigation Satellite System (GNSS)-based passive bistatic radar (PBR) which uses the GNSS signal as the illuminators of opportunity is studied for moving target detection (MTD). GNSS-based PBR has many advantages due to the removal of the transmitting device; however, its fundamental limitation is the low power density of the GNSS signal. Therefore, the integration time should be sufficiently long to obtain a promising maximum detectable range. On the other hand, the integration time is limited by the range migration and Doppler migration of the echo caused by target motion. In this letter, a novel MTD algorithm is proposed for the GNSS-based PBR, by employing a modified radon Fourier transform (MRFT) to achieve the required long-time integration for moving targets. The MRFT integrates the echo energy via joint searching of range, Doppler, and Doppler rate of the target, which can handle not only the range migration but also the Doppler migration problems, and significantly improves the signal-to-noise ratio (SNR) of the echo signal. An experiment using the GPS L5 signal as the illumination source is conducted and a moving car is successfully detected by the proposed algorithm, although significant range migration and Doppler migration are present due to variation of its speed.

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.

Global navigation satellite system (GNSS)-based passive bistatic radar (PBR) has many advantages benefiting from the bistatic configuration and the excellent properties of the GNSS signals. In this article, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is first evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this article, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multidimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multistatic positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least three satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5 km and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multistatic positioning experiment is also conducted and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multistatic operations.

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.

Global Navigation Satellite System Reflectometry (GNSS-R) is becoming a research hotspot in the remote sensing areas recently. This paper is focused on a new application of GNSS-R: Moving Target Indication (MTI). A modified GNSS-R based MTI algorithm is proposed. By the adding of a Doppler Frequency Modulation Rate (DFMR) estimation step, the modified algorithm could significantly improve the Signal to Noise Radio (SNR) of the target. An experiment is conducted for validation and the proposed algorithm performs well.

Real-time imaging products using Spaceborne Trinodal Pendulum synthetic aperture radar formation can provide valuable information in certain applications. The onboard orbit determination data of the spaceborne SAR platform is essential for the SAR imaging procedure. For real-time SAR imaging, the onboard orbit determination data is relatively low in accuracy compared with the orbit data obtained by off-line processing for the focusing of SAR data. The influence of errors in onboard real-time orbit determination data on SAR image quality should be considered. This paper proposes a Monte Carlo simulation model for inspecting the influence of onboard orbit determination data on imaging quality. This simulation model and its result may be helpful for the development of SAR real-time imaging focusing on providing terrain change information in a short time.

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.

The traditional particle filter detection algorithm cannot detect weak targets efficiently in the face of the current complex environment. To solve that problem, an improved particle filtering algorithm is proposed in this paper. Firstly, the basic theory of particle filter is introduced, the defects of particle filter method are pointed out. Utilizing auxiliary particle filter and resampling in filtering algorithm is proposed in this paper. So that particle degeneration can be solved which is the significant problem in particle filtering algorithm. A simulation experiment has been conducted to verify the effectiveness of the algorithm.

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.

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost, flexibility and real-time global coverage capability. In this paper, a novel high-order imaging algorithm is presented for GNSS-based bistatic SAR. Firstly, the modified equivalent squint range model (MESRM) is introduced to describe range history of the GNSS-based bistatic SAR, and the accuracy of the MESRM is analysed. Secondly, a high-order image formation algorithm based on the MESRM is derived, where the range cell migration (RCM) of the echo signal is corrected by two-step range cell migration correction (TSRCMC), and the azimuth-variance of the echo signal is solved by azimuth block hybrid correlation. Finally, simulation and experiment are performed to validate the presented algorithm.

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.

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.

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.

In space-borne azimuth multi-channel SAR, the antenna phase center spacing should be measured precisely to obtain the reconstruction filters. In this paper, a data-based onboard estimation method of antenna phase center spacing in spaceborne multi-channel SAR system is proposed. Firstly, the principle of data-based onboard estimation is presented, then the estimation method in details is described step-by-step. Finally, simulations are carried out, with two influence factors, SCR and focusing accuracy, to demonstrate the validity of the proposed estimation method.

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.