The target detection task of ground penetrating radar (GPR) based on deep learning has received widespread attention. Previous studies focused more on the features of targets in images and achieved excellent performance. However, in the practical application of these methods, GPR image is cut into several slices for feeding to the model for training and inference, which not only requires accumulating pulses but disrupts the continuity of pulse information, making it difficult to communicate semantic information of different parts of the same pulse in different slices. To address these issues, this letter proposes a pulse-by-pulse target detection model, namely, 1-D mix attention (MA) TransUnet, for GPR, avoiding pulse accumulation and preserving the continuity of pulse information. In structure, the spatial and channel mixed attention mechanism replaces skip connections in 1-D Unet, which effectively enhances the target features in pulse data. In addition, transformer block (TB) based on multihead self-attention (MSA) is applied to the downsampling feature map of 1-D Unet, which allows the model to effectively understand the global semantic information and suppress nontarget features that are similar to the target features in pulse data. Finally, the effectiveness of 1-D MA TransUnet is validated using GPR pulse data containing steel mesh as a case study. The model achieved an accuracy of 83.07%, a recall rate of 71.64%, and an F1-score of 76.93%, respectively.

GPR clutter removal significantly benefits subsequent target recognition, detection, and imaging, enhancing the subsequent processing quality. Traditional clutter removal approaches can only remove noise in simple environments. To solve this problem, We proposed a novel improved triplet attention u-net(ITU-Net) which focuses on the hyperbolic feature w e need while disregarding irrelevant ground clutter and other background noise. The ITU-Net network enhances image reconstruction capability and facilitates rapid image processing. The improved triplet attention module captures cross-domain interaction between any two domains between H, W, and C and considers long-distance dependencies separately in H, W, and C. The experimental results demonstrate that we can effectively retain the information of the hyperbolas while eliminating noise in complex environments.

Transformer has shown great capability in remote sensing and automatic target recognition (ATR). Due to the self-attention mechanism, the Transformer could extract global features while parallelizing training. However, the computational costs and power consumption are challenging the electronic computing techniques. Here, we develop a Transformer-based optronic neural network (TOPNN) for synthetic aperture radar (SAR) target recognition. We implement the self-attention mechanism in optics, significantly reducing the network computational costs. Compared with digital techniques, the TOPNN promises the speed of light, low computational costs, and low power consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility and efficiency of TOPNN for SAR target recognition.

Scattering imaging is a pervasive scenario in many areas, especially challenging the performance of remote sensing and automatic target recognition (ATR). Recently, deep learning was utilized for synthetic aperture radar (SAR) ATR in scattering scenarios by extracting the feature of speckle patterns. However, huge computational costs and power consumption challenge its development. Here, we develop a speckle-based residual optronic convolutional neural network (S-ROPCNN) for SAR target recognition. Specifically, we model the light scattering scenarios and build the optical imaging system to produce the speckle patterns for network training. The S-ROPCNN performs SAR target recognition in optical platforms with the speed of light, low computational cost, and low energy consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of S-ROPCNN for SAR target recognition in scattering imaging scenarios.

Marine surveillance radar can monitor the maritime environment under all weather conditions, but the presence of sea clutter significantly impacts its target detection performance. To effectively mitigate the influence of sea clutter on radar imaging, this paper proposes a neural network based on DenseNet combined with wavelet transform. The wavelet transform, known for its reversibility that preserves the original image information, is capable of extracting features at different levels of detail. DenseNet enhances the flow of extracted features, effectively alleviating the issues of gradient explosion or vanishing. The network is evaluated using data collected by the IPIX radar as the sea clutter noise dataset. Under various input conditions with different clutter-to-signal ratios, the proposed network achieves an average improvement of 18.78dB in clutter-to-signal ratio. Experimental results demonstrate the network’s effectiveness in suppressing sea clutter noise.

Synthetic aperture radar is a widely used technology. However, current 3D imaging algorithms either require a large number of passes or have limited elevation resolution due to carrier size. In this paper, a 3-D SAR imaging method based on 2-D wavefront modulation is proposed. The method achieves the same range and azimuth resolution as traditional SAR, while also obtaining high elevation resolution with limited carrier size and number of flights. The wavefront modulation function requires a smaller correlation coefficient between two elevation angles with a larger difference. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed method.

For reducing computational burden of electronic hardware and increasing practical recognition performance of optron-ic convolutional network (OPCNN), here we propose an optical backpropagation algorithm and realize the in-situ training OPCNN in optical platform for SAR target recognition. Training networks according proposed algorithm, major computational operations in forward and backward propagating process are all executed in optics with the speed of light and low consumption. Several experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of proposed in-situ training algorithm.

In this paper, a novel visualization method for GPR data interpretation and target analysis is proposed for underground target detection and recognition. After preprocessing, the original B-scan image has been transformed into two binary images containing potential targets. Then in the target extraction part, a novel row connection clustering algorithm is applied to separate all possible hyperbolic regions. Finally, a neural network is used to analyze the extracted slices and to estimate the parameters including the material and size of the targets. To illustrate the performance of the proposed method, this paper uses synthetic data generated from gprMax, which demonstrates the exciting accuracy of target detection and recognition.

Due to the atmospheric turbulence, defocusing, noise and other factors, the optical remote sensing image acquisition may become blurred. Therefore, it is critical of deblurring the images by algorithm. In recent years, neural network algorithms have shown excellent performance in optical re-mote sensing images deblurring. However, neural network algorithms have some limitations at the same time. They lack interpretability and need large amounts of training samples. The traditional deblurring algorithms are interpretable, but the performance is not as good as the neural network algorithms. In order to obtain an interpretable deblurring algorithm with good performance, this paper proposes a deblurring algorithm based on deep unfolding method, which is the combination of traditional algorithms and neural networks. It can achieve good performance and be interpretable at the same time. We demonstrate the effectiveness of the algorithm on remote sensing datasets with PSNR values and visual deblurring images. The experiments show the proposed algorithm has better deblurring results.

This paper presents a multi-structure extraction kernel dictionary learning (MSEK-DL) method for synthetic aperture radar (SAR) automatic target recognition (ATR). In order to extract the multi-structure features of SAR images for data enhancement and noise suppression, a matrix approximation method is used. Instead of using traditional linear dictionary learning method, non-linear kernel function is used to map the targets into a high-dimensional space, in order to obtain a better classification performance. The training method and optimization steps of MSEK-DL are presented in this paper. We carried out the experiment based on MSTAR dataset to demonstrate the effectiveness of the proposed classification algorithm. The experimental results show that the classifi-cation algorithm has better classification performance than some representative dictionary learning algorithms, espe-cially for small training datasets.

Imaging through scattering media is widely used in many research fields ranging from astronomical imaging to biomedical imaging. Here, we abstract the scattering media as a modulation model combining phase modulation and optical Fourier transform. Through simulation and experiments, the rationality of the model has been verified. Based on the modulation model, we can describe the scattering process with a mathematical model. From the mathematical model, we conclude that the speckle autocorrelation is the Fourier amplitude of the target image. In other words, by collecting the speckle pattern in the experiment and performing autocorrelation calculation on it, we can obtain the Fourier amplitude of the target image. However, the target image cannot be restored based on the Fourier amplitude information alone. The phase recovery algorithm can be used to recover the Fourier phase of the target image. In our experiment, the speckle autocorrelation results are consistent with the proposed model, and the target image is reconstructed successfully.

Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results.

Single image super-resolution technology is critical in remote sensing, effectively improving the resolution of target images, with super-resolution algorithms based on deep learning demonstrating superior performance. However, most neural networks present shortcomings, such as lack of interpretability and requiring a long training time, limiting them in some application scenarios. Moreover, due to multi-degradation factors, tasks put forward higher requirements for the adaptability of algorithms. Therefore, this work develops a structured deep unfolding network (SDUNet), which is adaptable and requires a lower training time by cascading multiple small network modules. Additionally, the unfolding strategy proposed deals with multiple degradations, fully exploiting prior knowledge. The suggested method is challenged against state-of-the-art neural network methods on one optical remote sensing image dataset and one natural image dataset. The experimental results demonstrate our method’s effectiveness in requiring less training time, involving fewer parameters, and achieving a higher reconstruction performance for optical remote sensing image super-resolution.

Single image super-resolution technology based on deep learning is widely applied in remote sensing. In recent years, the deep unfolding super-resolution strategy has been proposed, which combines the neural networks with traditional optimization-based algorithms, making the neural networks interpretable and achieving high performance. However, the typical deep unfolding algorithms usually treat different kinds of blurring kernels in the same way, so the algorithms cannot take advantage of the properties of blurring kernels, limiting the algorithm’s performance. To design a super-resolution network that can fully use the properties of Gaussian blurring kernels, a dual-resolution local attention unfolding network (DLANet) is proposed. Based on the Gaussian blurring functions, a low-resolution (LR) space branch is designed to supplement the high-resolution (HR) space branch. Specifically, for Gaussian blurring kernels, the closer the pixel is to the center, the greater the weight is. It means that the pixel points retained after downsampling will contain more information about the original corresponding pixel points, and it could be easier to estimate their original pixel values. So we design two branches. The HR branch completes the estimation of the whole image, and the LR branch only estimates the points retained after downsampling. To better complete the feature fusion of the two branches, we propose a row-column decoupling local attention module. This module can retain more information when fuse features and the row-column decoupling strategy can reduce computational complexity. Comprehensive experiments demonstrate the superiority of our method over the current state-of-the-art on remote sensing datasets.

Using deep convolutional neural networks to achieve automatic target recognition (ATR) is effective but it will bring heavy computation burden. Here we propose an optronic convolutional neural network (OPCNN) to realize ATR in optics. By using OPCNN, the computation cost is dramatically reduced and good performance of recognition accuracy is obtained simultaneously. Experimental results on Moving and Stationary Target Acquisition and Recognition dataset demonstrate the feasibility of our proposed OPCNN architecture.

Radar forward-looking imaging has important application value in target attack and terrain detection. However, traditional synthetic aperture radar (SAR) cannot perform high-resolution imaging of the area directly in front of the flight trajectory, due to the limitation of mechanism. This paper proposes a new airborne forward-looking imaging scheme based on maneuvering motion. The scheme firstly starts from the movement trajectory of the platform, gives the spatial geometric relationship of the forward -looking imaging, and establishes the echo signal model, further analyzes the azimuth resolution characteristics of the signal. On this basis, combined with the phase characteristics of the echo signal, a forward-looking imaging algorithm suitable for this scheme is derived. Finally, through simulation experiments, the target imaging results are analyzed to verify the effectiveness of the algorithm.

Multichannel Terrain Observation by Progressive Scans (TOPS) SAR has overcomed the system-inherent limitation of conventional synthetic aperture radar (SAR). Multichannel TOPS SAR is capable of imaging a scene with high geometric resolution and wide swath. But the complexity of algorithms to process multichannel TOPS raw data has increased. Multichannel TOPS takes much more time and computer resource to focus an image than conventional SAR does. On the other hand, optical computing has the advantages of high speed, huge capacity and low power. So we tried to use optical computing, especially optical two dimensional Fourier transform, to process multichannel TOPS raw data in this paper. Due to the limitation of system devices, we just partly realized the computing on a 4-f system. In our method, the raw data was firstly compensated to a signal which could be seen as the raw data of another Stripmap system without range cell migration. In the 4-f system, the Stripmap signal is then transformed into its two dimensional frequency domain by a lens and multiplied by a filter to focus the image. We had tested our system with simulation point target signals. The result showed that the proposed method could quickly process Multichannel TOPS data with resolution loss smaller than 11%.

Ground Penetrating Radar (GPR) is a non-destructive technique that employs electromagnetic waves to map subsurface structures. An effective simulation method is essential for GPR system design and signal processing. In this paper, a simulation method of GPR is proposed for dispersive media that is usually encountered in GPR applications. The behavior of dispersive media is simulated in our method by filtering the transmitted signal of GPR with an approximation of the system transfer function that is obtained by interpolating the values of the system transfer function evaluated at different frequencies, each of which is acquired by simulating GPR using the existing ray tracing based method of GPR simulation for non-dispersive media with a mono-frequency signal of the corresponding frequency as its transmitted signal. The accuracy and efficiency of our method were validated by comparing the experimental results of our method to those of the Frequency-Dependent Finite-Difference Time-Domain (FD-FDTD) method. The experimental results demonstrate that our method can simulate GPR for dispersive media with acceptable accuracy consuming much less running time than the FD-FDTD method for GPR simulation for dispersive media.

Target recognition is important in SAR applications. Conventional SAR target recognition is based on digital processing.A computer vision aided optical correlator is proposed in this paper. The method is implemented by the correlation between the SAR images and the target library in the optical domain. Ray tracing is used to generate the efficient and accurate target library, and the target parameters are adjustable. Finally, experiment results validate the proposed method.

In order to overcome the disadvantage that traditional multichannel SAR systems are only sensitive to the radial velocities of moving targets, a variable-boresight mode has been proposed, whose kernel is to detect and estimate the velocity component of the azimuth velocity at two nodes. For purpose of improving the estimation accuracy of the azimuth velocity, we introduce more nodes with different squint angles, so as to obtain more velocity components to carry out the estimation by the least square method. Considering that only few tiny angles can be ignored, while introducing more nodes are likely to bring about nonnegligible squint angles, the previously used signal processing suitable for side-looking mode is modified. Finally, numerical experimental results are provided to verify the effectiveness.

In processing of large scene synthetic aperture radar (SAR) images, the first step is to split them into tiles in order to reduce the load of computer's computation effort and memory, which is crucial in the follow-up procedures of building radar footprints detection or reconstruction. Compared to the traditional cockamamie gridding method, we propose an automatic sub-images extraction approach based on the density and distance-based (DD) clustering algorithm and the connected-component labeling (CCL) algorithm of the graph theory which can avoid a mass of unnecessary least error finding work. According to our method, the original image can be split without introducing excessive subjective operation, which can remain the primary information of buildings' images efficiently. Meanwhile, automatic area searching reduces manpower effectively.

A novel real-time optronic system for spectrum awareness in remote sensing is proposed, which provides ultra-broad bandwidth as well as high processing speed. A Mach-Zehnder modulator modulates the unknown RF signal to the light wave generated by a Laser Diode. Then a spatial time conversion device converts a serial optical signal in time domain to parallel optical signals in spatial domain. And thanks to the inherent ability of converging lens to perform two dimensional Fourier transform at the speed of light, the frequency spectrum information can be obtained much more easily and fast compared to conventional electronic wideband techniques by using analog-to-digital conversion. In this paper, principle and system design will be discussed, last but not least, simulation and equivalent experiment will be implemented to verify the performance of our system.

The development of phased array radar beamforming technologies has led to an ever-increasing demand for large antenna arrays and multiple beams. However, real-time processing becomes a difficulty for large array or multibeam beamforming due to huge data amount. To overcome the electronic mass data processing speed limitations, optronic technique is developed for beamforming in this letter. We present a novel real-time multibeam optronic beamforming (OPBF) system specially designed for large phased array. In our system design, beamforming is formulated as a finite-impulse response filtering process, of which radar raw data and weighting coefficients are encoded onto the laser beam by joint amplitude and phase control (JAPC) modules and two-dimension adding is performed by a lens. Ultrafast optical calculation and the proposed high-speed JAPC module make the system powerful for real-time processing. The proposed system has good expansibility in data capacity, which makes it applicable to massive data processing. What’s more, a practical low-power optronic beamformer is demonstrated. Measured results show that the presented OPBF system is able to accurately synthesize flexible and controllable multibeams and performs well in channel equalization.

In this paper, a new algorithm is presented to image ground moving targets in a synthetic aperture radar (SAR) system based on range frequency reversal transform-fractional Fourier transform (RFRT-FrFT). In this algorithm, a range compressed signal is initially transformed into the range frequency domain and then RFRT is proposed to directly compensate the range migration via multiplying the signal in the range frequency domain by its reversed data according to the equal interval sampling of range frequency variable, which can significantly decrease the computational complexity in target envelope migration elimination. Then, FrFT is applied to accomplish the target motion parameter estimation after range migration alignment. Finally, a ground moving target is well focused after motion compensation. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

Synthetic aperture radar (SAR) is easily affected by motion error during data acquisition. Motion error is very complicated and its range-dependent characteristic is studied in this paper. Compared with the original two-step motion compensation (MOCO), the proposed algorithm can compensate the residual range cell migration of scatterers away from the scene center. To realize it, the fast time scaling operation is performed. After it, we apply chirp scaling algorithm and form the image. Finally, the simulation experiment validates the proposed algorithm.

The improvement of synthetic aperture radar (SAR) resolution brings a broader applications but poses a great burden for SAR data processor. Real-time processing becomes a difficulty. Optronic technology has been developed for SAR realtime processing due to its ultrafast processing speed. A novel real-time optronic high-resolution SAR processor is proposed in this paper. It has the capability of full-resolution imaging. Restricted with the data scale of spatial light modulators (SLMs), SAR raw data cannot be all encoded onto the light beams at a time. To solve this, subaperture architecture is introduced in our system scheme. SAR data is optically processed in parallel by multiple optical subaperture processing modules and synthesized into a full-resolution SAR image. The module is implemented by multiple SLMs and lenses, which is innovatively proposed in this paper. The proposed system is applicable to large-scale SAR data processing, and the data scale is easy to extend with implementation of adding identical optical subaperture processing modules. Airborne SAR real data is used in the experiment, and a high-resolution image is also given. The PSLR of the focus results with and without subaperture partition are analyzed, which validates the satisfying image quality of the proposed algorithm.

Scattering mediums in atmosphere always leads to the decrease of imaging quality and performance in remote sensing, especially in Geological Surveying and Mapping, and also in Military Reconnaissance. So it's necessary to propose some methods to improve imaging quality. Here our primary results show that it's possible to measure medium's scattering property with adaptive algorithm through focusing, and both simulation and experiment results are given to validate the adaptive algorithm.

Due to the restriction of the number of receivers, it is hard to realize high-resolution wide-swath (HRWS) imaging and moving target detection simultaneously for multichannel synthetic aperture radar (SAR) systems. This paper presents a novel moving target detection method in HRWS mode. A digital beamforming (DBF) technique in azimuth time domain is proposed to convert the ambiguous multichannel SAR signals into unambiguous single channel SAR signals. Azimuth ambiguities of both stationary targets and moving targets can be effectively suppressed. Finally, moving targets can be successfully detected by traditional moving target detection methods for single channel SAR systems without adding receivers and changing SAR operating mode. Theoretical analysis and experiments showed the feasibility of the proposed method.

Digital beamforming (DBF) is wildly used in phased array radars, there are many algorithms and technologies to realize it. The traditional DBF architecture is based on digital signal processor. However, with the increasing requirements on target detection and resolution, the number of channel is getting more, the volumne of processing data is becoming larger, the capability of the digital signal processor is challenged. An optical system is proposed in this paper. The system is based on Digital Micromirror Deviece (DMD), it is designed to perform beamforming on receive for phased array radar. In this system, all calculation is realized in optical domain. That is, with the laser beam propogation, the calculation of beamforming is done. The system can form multiple beams simultaneously, the parameters of each beam can be controlled. The exprimental setup is built, and the beamforming results are given to validate the system performance.

Remote sensing is defined as the technology through which the characteristics of object on, above or even below the earths surface are identified, measured and analyzed without direct contact existing between the sensors and the targets. And nowadays, visible remote sensing plays an important role in providing accurate, large-scale information for many kinds of system. But visible remote sensing is easily influenced by the atmosphere. They are the scattering and absorption by the larger particles such as smoke, haze and fumes in the atmosphere, for which compensation is needed when correcting imagery. Here, we propose a method which could overcome these weakness and help visible remote sensing through the atmosphere. In this article, we are trying to take advantage of Transmission Matrix(TM) in optics to compensate for the effects of complex medium which is similar to the atmospheric particles in visible remote sensing. The principle of this way is that the TM can be exploited for focusing and image detection using well-established operators which used for inverse problems. We present the simulation and experiment results which validate the feasibility and effectiveness of the proposed method.

The Doppler centroid is a crucial parameter for the purpose of implementing azimuth processing for synthetic aperture radar (SAR) data. In this paper, we propose a scheme for estimating the Doppler centroid of conventional SAR. Virtual multi-channel SAR data can be generated by sub-sampling the original SAR data in azimuth. Consequently, each channel data is aliased and the spectrum components within each Doppler bin can be viewed as sources from different known directions. Then the Capon spectral estimator can be utilized to estimate the antenna pattern. Based on the estimated antenna pattern, we can obtain the baseband Doppler centroid of the original SAR data. Finally, experiments are provided to validate the effectiveness of the proposed algorithm.

With more degrees of freedom in the along-track axis, multichannel SAR systems are widely investigated for the purpose of ground moving target indication and motion parameter estimation. However, since conventional multichannel SAR system usually works at the side-looking mode and only the radial velocity is considered, it fails to detect targets only moving in azimuth. In this paper, the variable-boresight configuration is designed for multichannel SAR and overlapping imaging mode is applied to detect targets moving in azimuth. Besides, this system can also provide the precise velocity information of the moving target. Finally, the effectiveness of the proposed approach is verified with simulated multichannel SAR data.

This paper presents a new scheme for moving target velocity estimation in conventional high-resolution wide-swath (HRWS) SAR systems. A generalized steering vector is designed for azimuth ambiguity suppression and the velocity parameter of the moving target is involved in this modified steering vector. The low PRF in HRWS-SAR systems will result in serious azimuth ambiguity. When the adopted parameter is matched with the real velocity of the moving target, its azimuth ambiguities will be suppressed clearly. Entropy can be used to measure the performance for azimuth ambiguity suppression. The velocity can be estimated through searching the matched velocity. Finally, the effectiveness of the proposed approach is verified by simulated multichannel SAR data.

Multiple azimuth channels are usually employed to overcome the inherent limitation between high resolution and wide swath in synthetic aperture radar systems. However, unavoidable channel phase errors will significantly degrade the performance of ambiguity suppression. Conventional calibration methods usually regard these phase errors as constants during the whole observation time and ignore the azimuth-variant phase errors, which may not totally suppress azimuth ambiguities especially for very strong targets. This letter presents an azimuth-variant phase error calibration technique. The proposed technique first selects a strong point-like target as the calibration source. Then, the azimuth-variant phase errors can be estimated by comparing the phase of the calibration source and the corresponding steering vector in the range-compressed signals. Besides, a preprocessing method is presented to improve the calibration accuracy when the selected calibration source is affected by noise or interferences. Theoretical analysis and experiments demonstrate the feasibility of the proposed technique.

Multichannel synthetic aperture radar (SAR) signal reconstruction methods can effectively suppress azimuth ambiguities and achieve high-resolution wide-swath imaging. However, due to the characteristics of the practical antenna patterns, there exist non-bandlimited Doppler spectra, which will result in residual azimuth ambiguities, especially for strong targets. This letter presents a novel method for the suppression of the azimuth ambiguities of the strong point-like targets. First, we find out the positions of the strong point-like targets from the multichannel reconstructed SAR image. Then, we locate the ambiguous range history of each strong point-like target. Finally, the ambiguous components in the range history are filtered out by an orthogonal projection method. Therefore, the spectra of the strong point-like targets will be converted into the bandlimited spectra, and then, the azimuth ambiguities can be effectively suppressed by the conventional multichannel SAR signal reconstruction methods. Theoretical analysis and experiments demonstrate the feasibility of the proposed methods.

High-resolution and wide-swath synthetic aperture radar (SAR) imaging can be achieved by the azimuth multichannel system. The minimum variance distortionless response (MVDR) beamformer can be utilized to suppress azimuth ambiguities. However, the presence of channel phase errors significantly deteriorates the performance of the azimuth multichannel SAR system. Instead of employing subspace techniques, this letter proposes a robust channel phase error calibration algorithm via maximizing the MVDR beamformer output power. Compared with the conventional subspace-based calibration methods, there is no redundancy of channels required to estimate the subspaces in the proposed algorithm. Also, the proposed algorithm is relatively robust, because it avoids the subspace swap phenomenon, which probably takes place at low signal-to-noise ratios for the subspace techniques. Moreover, the proposed method has the advantage of estimating the channel phase errors without covariance matrix decomposition, which reduces the computation load. The simulation experiments and the real data processing validate the effectiveness of the proposed calibration method.

Linear frequency modulated (LFM) continuous wave (CW) synthetic aperture radar (SAR) is promising in airborne earth observation with compact, lightweight, cost-effective and high resolution advantages. For CW imaging radars, however, weak targets may be submerged by sidelobes of strong range interferences including transmitter leakage and nadir signal, especially when the slant range is far. This paper firstly discusses how to suppress transmitter leakage and nadir signal suppression for LFMCW SAR by choosing appropriate pulse repetition interval (PRI) and digital sampling frequency. Although the transmitter leakage could be weakened by an appropriate PRI and finite impulse response (FIR) filter, the residual leakage is so strong that its sidelobes will still submerge some weak targets. Then, a novel transmitter leakage canceling method based on orthogonal projection is derived, which could effectively reduce the sidelobe level of transmitter leakage. Theoretical analysis and experiments on a real unmanned LFMCW SAR system showed the efficiency of the proposed method.

Synthenic Aperture Radar (SAR) has an important role in the field of remote sensing. As optical SAR data processor has the outstanding advantage of high speed, it performs processing prospect in real-time SAR imaging field. Liquid crystal based spatial light modulators (SLMs) are widely used to make phase modulation in optical SAR processor. However, in some phase-sensing or real-time applications such as SAR imaging process, low phase precision of SLM may cause phase error and low data refresh rate of SLM may restrict processing speed. This paper proposes a new optical SAR data processor with high-speed and high-precision phase modulation. The core of this optical processor is a phase modulation module by using a digital micromirror device (DMD) which can achieve high-speed and high-precision phase modulation because of its high data refresh rate of 9500Hz and phase resolution of 0.002 rad. The principle of phase modulation with DMD and the structure of the new SAR optical processor are presented. The images processed by it are also presented.

Along a roughly chronological order, the azimuth signals undersampled from the multichannel synthetic aperture radar (SAR) for high-resolution wide-swath (HRWS) imaging correspond to recurrent nonuniform sampling. Only a finite-duration sequence of samples of the azimuth signal can be obtained in practical applications. A new recurrent nonuniform sampling scheme can be generated by extending these samples periodically across the boundaries provided that the azimuth signal is bandlimited. Thus, from the perspective of reconstructing recurrent nonuniform sampling, an innovative reconstruction algorithm for suppressing azimuth ambiguities of multichannel HRWS SAR is proposed, which is suitable to be implemented on digital computers. Furthermore, the presented algorithm acquires the filter weights without matrix inversion reducing tremendously the computational load.

A method to solve multi-target classification problems with unknown clutter parameters is proposed in this paper. The unknown parameter is estimated and synthesized at each observation, and probability of each hypothesis is updated. Subsequently, the optimal waveform for the next illumination is designed based on NP criteria, and the final decision is made based on the sequential probability ratio testing. Simulated results are presented based on our method and show that the optimal waveform-based sequential testing can be decided through reduction of the average illumination number. Furthermore, results indicate a significant improvement over the non-optimal waveforms.

Radar performance improvement through waveform optimization has been an ongoing topic of research recent years. In this paper, we use the optimal waveform design method to deal with the moving target in the clutter and noise. Neyman-Pearson detector criterion is used to maximize the probability of target detection. The optimal waveform is then designed theoretically corresponding to the velocity of target and clutter/noise power spectrum density. Simple CW signals can produce maximum detectability based on different noise PSD situations. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.

A system design of optronic high-resolution synthetic aperture radar (SAR) processing is proposed in this letter; it has the capability of adaptive phase error compensation. In our system design, SAR raw data are focused optically by phase correction in the two-dimensional frequency domain. The computations of SAR image formation are performed by spatial light modulators and lenses. With the propagation of the laser beam, the imaging results can be captured by the charge-coupled device. The phase error can be estimated from the imaging results, and adaptive phase error compensation is implemented in the optronic processing. Phase error compensation makes the optronic processing robust and flexible; it can be used for both the initial system calibration and the focus quality improvement. Finally, the experimental setup is demonstrated. The entropies of the images with and without phase error compensation are analyzed, which validates its performance on improving the focus quality. The real data results of an airborne SAR and RADARSAT are given.

Multichannel synthetic aperture radar systems in azimuth can effectively suppress azimuth ambiguity and are promising in high-resolution wide-swath imaging. However, unavoidable channel errors will significantly degrade the performance of ambiguity suppression. Conventional subspace calibration methods usually estimate phase error via decomposing a Doppler-variant covariance matrix from one Doppler bin, and then average these errors estimated from several Doppler bins to improve the estimation accuracy, which will result in a large computational load. This letter presents an improved channel error calibration method, which works on the undersampled data of the individual azimuth channel. By a proposed matrix transformation method, the Doppler-variant covariance matrices will be transformed into a constant covariance matrix. Therefore, the improved calibration algorithm needs to estimate and decompose the new covariance matrix only once. The computation load could be greatly reduced. Moreover, the new covariance matrix can be estimated by training samples not only from range bins but also from Doppler bins, which will improve the estimation accuracy. Theoretical analysis and experiments based on simulations and measurements showed the high accuracy, efficiency, and robustness of the improved method, particularly in low signal-to-noise ratio.

Synthetic Aperture Radar now is widely used in remote imaging which have many kinds of system. But as SAR data become larger and larger, the traditional Digital Signal Processing processor can not afford the high speed and high resolution process in rated size and power. But our optical processor for SAR can finish the data process in high speed because it can perform the Fourier transformation at the speed of light with low power consumption. Besides, this system is automatic and programmable which can be realized by computer and spatial light modulator.

Microwave staring imaging scheme based on range pulse compression and azimuth wavefront modulation is a new radar scheme. Under the condition of no relative motion between radar and scene, the scheme can obtain radar images with high resolution in both range direction and azimuth direction. The paper presents this imaging technique by theoretical analysis and simulation. Microwave staring imaging structure is introduced firstly. Then, requirement of the azimuth wavefront modulation and target change detection is analyzed.

In this paper, we combine the waveform design method with the synthetic aperture algorithm to suppress clutter and generate clear microwave images of targets. The linear recursive model is introduced into the SAR operation principle and Kalman filter algorithm is used to estimate target and clutter responses in each azimuth direction based on their states before, which both are assumed to be Gaussian distributions. Optimal waveforms based on NP criteria are designed repeatedly and used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. The simulations show that our algorithm can significantly reduce the clutter response while targets are imaged.

Optronic processing is a promising way to real-time highresolution SAR image formation. Rang migration is inevitable in SAR imaging, especially in the case of highresolution SAR system, range cell migration correction (RCMC) is considered in nature. A solution of RCMC by using optronic processing is presented in this paper. In this optronic solution, most computation is undertaken by optical devices, while the data control is undertaken by electronic devices. The architecture of the experimental setup is presented, and the experimental results are given.

An automatic DPCA technique is presented for SAR Ground Moving Target Indication (GMTI). We note that there exists a shift and a phase difference between the images from two channels. Therefore, SAR-GMTI can be implemented in the following steps: Image registration, phase compensation, image subtraction, and CFAR detection. In our technique, these steps are carried out automatically, and thus no precise information is needed about the length of the baseline and the velocity of the platform. We utilize a set of real data to demonstrate the accuracy and the robustness of our algorithm.

Synthetic Aperture Radar (SAR) is a powerful tool for alltime and all-weather imaging. As the technology developed, the resolution of SAR image is required much higher, and the processing time is required shorter. Traditional DSP processor is difficult to process the high resolution complex SAR data of 3 meter and 1 meter, or even high. The optical processor performs the most promising capability, since the Fourier lens provide inherent parallel computing. In addition, it can perform the Fourier transformation at the speed of light. This paper proposes a tiling project to perform the full resolution SAR data imaging.

An innovative algorithm to reconstruct complex target-induced azimuth envelope in synthetic aperture radar (SAR) system is proposed. Unlike the assumption in conventional SAR imaging algorithms, target's backscattering coefficient can hardly remain constant when synthetic aperture time is long enough. In order to fully understand the target feature, we extract both amplitude and phase information of target-induced azimuth envelope from SAR raw data. In this paper, we formulate range migration curve (RMC) with a parametric model and implement curve fitting to estimate these parameters. The input pixels of curve fitting process is extracted from range compression result of raw data. Applying the idea of random sample consensus (RANSAC), this algorithm classifies pixels according to the target's RMC they belongs to, and extracts target's feature information at the same time. Experimental results are conducted to validate this algorithm.

In this paper, we proposed a clutter suppression algorithm for SAR imaging due to different target power spectrum density (PSD) and clutter PSD in azimuth direction. The optimal waveform of the SAR system is designed according to the prior-knowledge of the target and clutter, an amplitude limiter set in frequency domain is used to suppress the clutter response and 2-dimentional pulse compression is then used to the SAR imaging. Simulated results are presented based on our method which is shown great improvement in target image over clutter image. Then conclusions are drawn based on our analysis and simulations.

Conventional SAR imaging algorithms form SAR image based on imaging geometries, the resulting image indicates the spatial location of illuminated targets with respect to radar platform. Scattering characteristics of targets are overlooked by these algorithms. We construct a model of received SAR signal in terms of (1) envelope, (2) phase, (3) duration, (4) arrival time, (5) frequency center and (6) chirp rate, and the envelope shape can be controlled by two parameters. Arrival time, frequency center and chirp rate indicate the spatial location and motion dynamics of the targets, while information of scattering characteristics can be analyzed from the other parameters. The parameters of the model are then estimated via atomic decomposition. In this paper, the impact of target-induced envelope on data focusing is analyzed, furthermore, target feature can be extracted from a signal-level point of view.

The conventional SAR imaging algorithms are developed based on specified imaging geometry models, and these algorithms become more complex considering finer resolution or more complicated geometry. The quality of the product is measured by resolution, PSLR and ISLR, etc. without regard to its application. This paper proposes a signal-model-based imaging scheme. Independent of imaging geometry, the new scheme focuses the backscattered echoes by estimating the signal parameters. Furthermore, two realizations of the scheme are presented. This scheme will also have great potential for target feature extraction and image interpretation.

Dual-antenna single-pass synthetic aperture radar interferometry needs alignment of both antenna beams to achieve the best interferometric performance. Meanwhile, geosynchronous synthetic aperture radar, potentially used for global earthquake prediction and many other attractive applications, also requires antenna pointing control system to steer the radar antenna to illuminate desired territory, otherwise, even slight deviation from ideally boresight direction can cause a great variation of footprint position, because of the large slant range from the radar to mapped area. In principle, it is possible to measure antenna pointing information directly; however, measurement uncertainties will limit the accuracy. Thus it is feasible to resort to received radar data to measure the antenna pointing. This paper concentrates on the antenna pointing measurement using onboard Doppler centroid estimator, and furthermore, to drive pitch and yaw angles to steer the antenna pointing. In order to realize real-time onboard processing, a novel Doppler centroid estimation algorithm, called sign-MLCC, is presented here, utilizing the phase information of the received signal and the arcsine law by analyzing the sign alone, then evaluation of the algorithm is discussed. Finally, simulations are shown to prove the validity and reliability of the proposed method.