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In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state‐of‐the‐art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP‐hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors' approach on the D‐Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods. [ABSTRACT FROM AUTHOR]

In view of the increasing number of space objects, comprehensive high‐quality space surveillance becomes ever more important. Radar is a powerful tool that, in addition to detection and tracking of objects, also enables spatially high‐resolution imaging independent of daylight and most weather conditions. Together with the technique of Inverse Synthetic Aperture Radar (ISAR), very high‐resolution and distance‐independent two‐dimensional images can be obtained. However, advanced high‐performance radar imaging of space objects is a complex and demanding task, touching many technological and signal processing issues. Therefore, besides theoretical work, the Microwaves and Radar Institute of German Aerospace Center (DLR) has developed and constructed an experimental radar system called IoSiS (Imaging of Satellites in Space) for basic research on new concepts for the acquisition of advanced high‐resolution radar image products of objects in a low earth orbit. Based on pulse radar technology, which enables precise calibration and error correction, IoSiS has imaged space objects with a spatial resolution in the centimetre range, being novel in public perception and accessible literature. The goal of this paper is therefore to communicate and illustrate comprehensively the technological steps for the construction and successful operation of advanced radar‐based space surveillance. Besides the basic description of the IoSiS system design this paper outlines primarily useful theory for ISAR imaging of objects in space, together with relevant imaging parameters and main formulae. All relevant processing steps, necessary for very high‐resolution imaging of satellites in practice, are introduced and verified by simulation results. Finally, a unique measurement result demonstrates the practicability of the introduced processing steps and error correction strategies. [ABSTRACT FROM AUTHOR]

This paper illustrates an interferometric inverse synthetic aperture radar based on three‐dimensional imaging and rotational velocity estimation from a multistatic setup with three ground‐based radars with non‐orthogonal baselines. The radar backscattering along the orbit is simulated based on physical optics approximation. The paper illustrates a novel shape‐dependent and rotation‐invariant feature called normalised eigenvalues for classification. A database of interferometric point clouds is generated using radar back scattering at different aspect angles and classified using various classification algorithms. The classification accuracy at varying SNR levels was evaluated using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains. [ABSTRACT FROM AUTHOR]

Classification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples. [ABSTRACT FROM AUTHOR]

Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart. [ABSTRACT FROM AUTHOR]

The usual procedure for extracting ocean surface information from data acquired from a high‐frequency surface wave radar transmitting from a floating platform is to first compensate for the motion of the antenna in the acquired motion‐contaminated Doppler spectrum and then extract the ocean wave parameters from the motion‐compensated result. The authors propose a new real‐time method to estimate the significant wave height directly from the antenna's received electric field in the time‐domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analysing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the significant wave height with a root‐mean‐square error (RMSE) between 10 and 14 cm over a wide range of significant wave height values. [ABSTRACT FROM AUTHOR]

As a new algorithm to achieve sparse aperture inverse synthetic aperture radar (SA‐ISAR) imaging, the alternating direction method of multipliers (ADMM) suffers from selection of model parameters and the inability to discriminate the target and non‐target components. Therefore, an improved ADMM (I‐ADMM) algorithm is proposed to solve these problems. First, the particle swarm optimisation (PSO) algorithm is utilised to obtain the optimal model parameters. Aiming to adequately preserve the target's shape information in the ISAR image, a modified image entropy (IE) is proposed to be the objective function of the PSO. Then, a new regularisation function is obtained from the ADMM based ISAR image. The proposed algorithm can enhance the target components and suppress the non‐target components by utilising the regularisation function. Finally, results based on the simulated and measured data show that the proposed I‐ADMM algorithm not only achieves at least 9.4% improvement in IE but also greatly improves the image quality as visually compared with the traditional ADMM algorithm which validates the effectiveness and superiority of the proposed algorithm. [Correction added on 13/09/23, after first online publication: In the following sentence in abstract "Finally, results based on... the proposed algorithm." 10% was changed as 9.4%.] [ABSTRACT FROM AUTHOR]

Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and ice‐water interface detection in Antarctica. It is designed and operated by the British Antarctic Survey from 2014. With multiple antennas, oriented along and across‐track, for transmission and reception, it enables polarimetric 3D estimation of the ice base with a single pass, reducing the gridding density of the survey paths. The off‐line data processing stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based on back‐projection, for along‐track and range dimensions; and finally, a direction of arrival estimation (DoA) of the remaining across‐track angle, by modifying the non‐linear MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17 and 19/20 seasons, assessed and validated the instrument and processing performances. Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the 3D sensing capabilities. By resolving directional ambiguities and accounting for reflector across‐track location, the true ice thickness and bed elevation are obtained, thereby removing the error of the usual assumption of vertical DoA, that greatly influence the output of flow models of ice dynamics. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar tomography (TomoSAR) baseline optimization method is capable of reducing system complexity and improving the temporal coherence of data, which has become important research in the field of TomoSAR. A nested TomoSAR technique is proposed, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co‐array through the nested array to increase the degrees of freedom of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co‐array, covariance matrix of the echo needs to be obtained. Therefore, a TomoSAR sparse reconstruction algorithm based on the nested array is proposed, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. The effectiveness of the proposed method through simulated and real data experiments is demonstrated. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of this proposed method have better anti‐noise performance and retain more image information. [ABSTRACT FROM AUTHOR]

Owing to the time variance of the inverse synthetic aperture radar (ISAR) imaging plane of space targets, scatterers with large heights relative to the imaging plane migrate through several Doppler cells. This defocussing in the azimuth direction of each scatterer cannot be compensated via the traditional migration through the resolution cell (MTRC) compensation method because the height of each scatterer is quite different and independently distributed in the ISAR image. In this study, a parametric azimuth‐defocussing model‐based CLEAN method is proposed for the space‐target ISAR image. First, high‐order defocus parameters and their influences on the image of space targets were analysed at different arc segments. Subsequently, a mathematical azimuth‐defocussing expression of the ISAR image scatterers was derived after MTRC and azimuth high‐order phase compensation, and an approximate defocus expression based on the Fresnel integral was provided for computational efficiency. This model can estimate the complex amplitude, coordinates and defocus parameters of each scatterer in the image domain by applying a sequential quadratic programing algorithm. Finally, the validity and stability of the proposed algorithm for the isolated and coupled scatterer extraction were verified at different signal‐to‐noise ratios, and different defocus parameters were verified through simulations. [ABSTRACT FROM AUTHOR]

Spaceborne interferometric synthetic aperture radar (InSAR) technology is an effective method to obtain digital elevation model (DEM) data. The bistatic InSAR configuration of the inclined‐geosynchronous (InGEO) transmitter with low earth orbit (LEO) receivers (InGEO‐LEO) is a novel InSAR system to acquire terrain information. This novel system is characterised with high resolution, wide swath and timeliness, but the Newton iterative method is time‐consuming to solve the bistatic InSAR equations for fast DEM generation. For the conventional LEO bistatic InSAR system, the closed‐form solution is an effective method to improve the efficiency of solving InSAR equations, which is invalid in the InGEO‐LEO InSAR system because of the significant geometry difference caused by the orbits of InGEO transmitter and LEO receivers. To address this issue, we analyse the bistatic InGEO‐LEO geometry in detail and exploit the bistatic InSAR equations to propose an approximate closed‐form solution (ACS) for the novel system. Compared with the general Newton iterative method, the ACS significantly improves the efficiency of geolocation for the bistatic InGEO‐LEO InSAR system with high precision. Simulation experiments are carried out to verify the effectiveness and superiority of ACS. [ABSTRACT FROM AUTHOR]

A method for time‐domain motion compensation of high frequency (HF) radar signals for the case of a floating transmitter and fixed receiver is proposed when the motion parameters (including the amplitude and angular frequency of the motion) are not known a priori. In this study, the floating platform is assumed to follow a single‐frequency motion model. Additionally, instead of trying to estimate platform motion parameters from the received motion‐contaminated Doppler spectrum, which is proportional to the observed radar cross‐section of the ocean surface from the floating platform, the motion parameters from the autocorrelation function of the received electric field are estimated, which is related to the received radar cross‐section by application of an inverse temporal Fourier transform. By comparing the locations of the zeros of the autocorrelation function for the fixed antenna case with that for an antenna on a floating platform and finding the zeros associated with the platform motion, the motion parameters are estimated. These parameters are matched with actual motion parameter values, from which the motion‐compensated Doppler spectrum is recovered from the Doppler spectrum of the antenna on a floating platform. [ABSTRACT FROM AUTHOR]

In order to recover the three‐dimensional (3D) structure of the target from sequential inverse synthetic aperture radar (ISAR) images, the factorisation method is generally used. It requires a large number of high‐quality matched feature points from different ISAR images. If the number of extracted feature points is insufficient, the restored 3D structure is not obvious. Furthermore, the mismatching of feature points will greatly affect the quality of target reconstruction. However, the factorisation method only uses the information from the ISAR images, while that from imaging geometry is not sufficiently considered. ISAR imaging is a kind of projection, and the projection plane information could be taken into account for the 3D reconstruction. Hence, a new 3D reconstruction method for stable targets from sequential ISAR images is proposed in this paper. Firstly, the ISAR images are preprocessed with the CLEAN algorithm. The maximum between‐cluster variance method is applied to extract feature points from the processed images. Moreover, the image projection plane and projection equation corresponding to different ISAR images are analysed by using imaging geometry information. According to the projection equation, the feature points are back‐projected (BP) to the 3D space. Finally, the 3D point clouds obtained by the BP from multiple radar images are fused by the iterative closest point algorithm to restore the 3D structure of the target. The simulation and experiment results show the effectiveness and robustness of this method. [ABSTRACT FROM AUTHOR]

Generalisation of one‐dimensional dictionary learning (1DDL) algorithms to Multidimensional (MD) mode and its utilisation in MD data applications, increases the speed and reduces the computational complexity. An example of such an application is 3D inverse synthetic aperture radar (ISAR) image reconstruction and noise reduction. In this study, in addition to MD mode generalisation, the formulation structure of the multidimensional dictionary learning (MDDL) problem is discussed followed by two novel algorithms to solve it. The first one is based on the K‐singular value decomposition algorithm for 1DDL, which uses alternating minimisation and singular value decomposition. The second algorithm is the extension of the sequential generalisation of K‐means 1DDL algorithm to the MD mode. Moreover, the MD tensor denoising method based on the MDDL algorithm (MDDL‐ALG) is proposed. As an application, the proposed method is used to denoise the 3D ISAR image. The numerical simulations reveal that the proposed methods, in addition to reducing the memory consumption and the computational complexity, also enjoy higher convergence rate in comparison to 1D algorithms. Specifically, convergence speed of MD algorithms, depending on the training data size, is up to at least 10 times faster than the equivalent 1D counterparts. As revealed through the simulations, the amount of signal to noise ratio recovered by the proposed methods is almost 2 dB higher than the case using a pre‐designed dictionary for denoising. Moreover, it outperforms about 10 dB over the case with the conventional 3D‐IFFT method for image construction. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation of mixed near‐field (NF) and far‐field (FF) signals is one of the key issues in the study of array signal processing. For more precise localisation performance, sparse arrays have drawn the attention of an increasing number of academics. Excitingly, sparse arrays enable larger array apertures and higher degrees of freedom (DOFs) than traditionally used uniform linear arrays. Consequently, this study proposes a transformed symmetric nested array (TSNA) for the passive localisation application regarding mixed NF and FF. There are three processes that go into building the proposed array. In the first step, we swap the positions of the uniform linear arrays and the sparse linear arrays in the nested array. In the second step, we place the corresponding array sensors extracted from the uniform linear array behind the uniform linear array to form a new subarray. In the last step, the TSNA is constructed by folding the formed array in half. Furthermore, we give the formula of the largest consecutive lags of the TSNA and its corresponding constructor. Finally, through theoretical analysis and computer performance simulation, this study illustrates the proposed array's numerous advantages over currently used symmetric sparse arrays. [ABSTRACT FROM AUTHOR]

Sparse inverse synthetic aperture radar (ISAR) imaging can be generally achieved by compressed sensing (CS) methods because sparse sampling disables the approach of conventional range Doppler. However, the CS‐based methods have to convert the matrix into a vector when the random sampling is adopted, which results in huge memory usage and high computational complexity. Note that matrix completion (MC) is suitable for matrix operations, which can recover random sparse data directly based on the low‐rank property of the echo matrix. In order to improve the performance of the conventional MC approaches, a novel reweighted matrix completion method for sparse ISAR imaging is proposed in this paper. To avoid using the same singular value thresholding of the traditional MC method to achieve a low‐rank solution, the reweighted scheme for singular values is applied to restrain the rank. Furthermore, the weights of the current iteration are updated with their singular values obtained in the former iteration, rather than using a fixed value. Then, the alternating direction method of multipliers is utilised to alternatively optimise the proposed model, which has an improved computational efficiency. Once the full data is recovered, the ISAR image can be achieved via the conventional 2D inverse fast Fourier transform. Experimental results based on measured data validate the proposed approach that can result in improving imaging performance within several seconds, which is tens of times faster than some reported low‐rank‐based methods. [ABSTRACT FROM AUTHOR]

A polarimetric synthetic aperture radar (POLSAR) system provides an image that can be considered as a data cube containing spatial information in two spatial dimensions and polarimetric information in the scattering dimension. A spatial and polarimetric residual network (SPRN) is proposed for POLSAR image classification. At first, polarimetric features are extracted from the scattering dimension through two designed polarimetric residual blocks. Then, the processed POLSAR cube is fed to two consecutive spatial residual blocks for contextual feature extraction. Three dimensional convolutional layers are used as basic layers for simultaneous extraction and fusion of polarimetric information and correlation among neighbouring pixels in local regions. The shortcut connections are utilised to overcome the degradation problem due to increasing network depth. In addition, batch normalisation is applied to regularise the learning process. The experimental results on four real POLSAR images show the superior performance of SPRN compared to several state‐of‐the‐art classifiers in terms of various assessment measures. [ABSTRACT FROM AUTHOR]

In recent years, bistatic inverse synthetic aperture radar (Bi‐ISAR) imaging of targets with sparse aperture has attracted more and more attention. One of the critical issues is that the Bi‐ISAR imaging of some large targets with rotational motions is prone to migration through resolution cells, which increases the imaging difficulty. In order to enhance the compensation for through resolution cell migration in those scenarios and improve the accuracy and resolution of Bi‐ISAR images under sparse aperture, that is, improving the target recognition of Bi‐ISAR, the authors aimed at the high‐order migration term and the high‐order phase error term and proposed a Bi‐ISAR sparse aperture high‐resolution imaging algorithm based on complex Bayesian compressed sensing and frequency resampling. The algorithm was solved by 'distributed' iteration. The migration compensation under the sparse aperture conditions was completed, and the quality of the reconstructed images was improved. Simulation results verified the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Moving target detection performance is seriously affected by missing array elements for multichannel synthetic aperture radar (Multi‐SAR) systems. Meanwhile, there is a contradiction between the accuracy of data recovery and the computational burden in traditional methods. To solve the problems, a moving target detection method based on CUR‐RPCA for missing array elements is proposed in this paper. First, key nodes of the echo data are extracted and rearranged into a low‐rank matrix. Then we uses the low‐rank characteristic to complete key nodes of data. Subsequently, complete echo data is constructed by utilising recovered key nodes of data and CUR decomposition. Therefore using the ideas of CUR and RPCA, a novel optimization problem is constructed to achieve separation of moving targets and clutter while recovering data. In addition, only key nodes of the data is completed to achieve the performance of all data recovery, so that the disadvantage of low computational efficiency has been overcome for traditional matrix completion methods. Finally, the superior clutter suppression performance of the proposed method is verified by several numerical simulations and comparative studies. [ABSTRACT FROM AUTHOR]

In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped‐random volume over ground, the terrain‐compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L‐Band Synthetic aperture Radar (PALSAR) dataset. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging of targets closely behind a reinforced concrete wall, modelled by a periodic structure by using the proper Green's function (GF) of the structure, is studied. To compute the GF accurately, using the rigorous coupled‐wave analysis (RCWA) is proposed. The radiation of a line source in the spectral domain as a summation of plane waves is represented. Illuminating the periodic structure by each plane wave, Bragg modes by RCWA are computed. Then, the GF is estimated as the superposition of all the Bragg modes' contributions. The numerical Finite Element Method results of COMSOL validate the results of our semi‐analytical approach. Having COMSOL numerically calculated backscattered fields of a target behind the wall and the simple free‐space GF, the synthetic aperture image of a target is not focussed. Employing the phase of the proper GF instead of the simple free‐space GF, the target image is successfully refocussed. The effect of the number of Bragg modes on the estimation of the GF and also uncertainties in the distances between reinforced metallic bars on the target image is investigated. [ABSTRACT FROM AUTHOR]

Although deep neural network technology brings high recognition accuracy to the field of synthetic aperture radar image‐based automatic target recognition, it also produces the catastrophic forgetting problem. Here, a new incremental learning method that can extract more information about old data is proposed. Based on the rehearsal method, the authors' method adds extra linear layers after the feature extractor of the network before training on new incremental data and uses the network to generate distilled labels for incremental training. Through experiments on the moving and stationary target acquisition and recognition data set, we conclude that, when the old model has good performance, our method has better performance than other typical incremental learning methods on small data sets. [ABSTRACT FROM AUTHOR]

It is difficult to obtain a large number of high‐quality labelled Synthetic Aperture Radar (SAR) image data. In order to solve the classification task of an SAR image with limited data, this paper proposes a dual‐network classification model of pre‐training and fine‐tuning based on contrastive learning and pseudo‐label training strategy. The algorithm firstly obtains the initial weight of the network through unsupervised pre‐training and then takes advantage of the pseudo‐label information to realise the fine tuning of the dual network. It effectively reduces the number of samples required during classification. Compared with the classification network without optimisation strategy, the proposed algorithm achieves higher classification accuracy in the ablation study. [ABSTRACT FROM AUTHOR]

With the Loffeld's bistatic formula, the point target reference spectrum (PTRS) of multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi monostatic term and bistatic deformation (BD) term. The former term is analogous to the monostatic SAS PTRS while the latter one describes the bistatic phase introduced by the bistatic displacement between the transmitter and receiver. The multireceiver SAS echo signal can be transformed into monostatic SAS equivalent data after correcting the BD term. Based on the range sub‐block processing method, this data transformation is conducted by using the preprocessing step. Then, fast imaging processors based on monostatic SAS system can be directly exploited to process the monostatic SAS equivalent data. In this paper, the authors mainly focus on the application of non‐linear chirp scaling algorithm. At last, the simulations and real data are used to validate the presented method. The processing results show that the imaging performance of presented method is nearly close to that of traditional method. Besides, the imaging efficiency is highly improved compared to the phase centre approximation (PCA) method. [ABSTRACT FROM AUTHOR]

By generalising dictionary learning (DL) algorithms to multidimensional (MD) mode and using them in applications where signals are inherently multidimensional, such as in three‐dimensional (3D) inverse synthetic aperture radar (ISAR) imaging, it is possible to achieve much higher speed and less computational complexity. In this study, the formulation of the multidimensional dictionary learning (MDDL) problem is expressed and two algorithms are proposed to solve it. The first one is based on the method of optimum directions (MOD) algorithm for 1D dictionary learning (1DDL), which uses alternating minimisation and gradient projection approach. As the MDDL problem is non‐convex, the second algorithm approximates the non‐convex objective with a new jointly convex function and efficiently solves it. As an application, we use the proposed methods to restore and denoise the ISAR image. Numerical experiments highlight that the proposed algorithms, in addition to reducing the computational complexity and the amount of required memory, also entail less training data for learning the dictionary, and enjoy higher convergence speed in comparison to their one‐dimensional (1D) counterparts. Specifically, convergence speed of MD algorithms, depending on the size of the training data, is up to at least 10.7 times faster than the equivalent 1DDL algorithm. According to the simulation results, the SNR value achieved by the proposed algorithms is higher than the case where we use the 3D‐IFFT for image reconstruction and the case of fixed dictionaries, by approximately 12 and 4 dB, respectively. [ABSTRACT FROM AUTHOR]

A translational motion estimation and compensation method is proposed based on the parametric method by multiple stations in the distributed inverse synthetic aperture radar (ISAR) imaging. For ship targets with complex motion, the distributed ISAR increases the cross‐range resolution of the image by expanding the observation angle. However, the translation compensation methods by a single station will destroy the correlation between the echoes, which reduces the image quality, and a more accurate joint translation compensation method is required. Multi‐static observation is obtained in the Distributed system, which we can utilise in the parameter estimation: 1) joint Doppler estimation, 2) station angle estimation, and 3) rotation velocity estimation. Based on the above estimations, the velocity vector and the acceleration vector of the target can be obtained, and then the translational motion compensation can be realised parametrically. The algorithm processing chain is presented in this paper. Finally, the numerical simulations with four stations are presented to verify the effectiveness and the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a new interferometric inverse synthetic aperture radar (InISAR) image registration method under squint model based on phase cancellation compensation of combined processing data. Firstly, the residue translational motion component (RTMC) in range profile and the mismatching in the ISAR images induced by joint motion compensation with the same parameters to each antenna are analysed. Then, the RTMC compensation is applied to re-align the range profile sequences of the received antenna. The phase cancellation compensation is carried out to eliminate the mismatching in the range and azimuth direction, respectively. Simulation results demonstrate that the mismatching between the ISAR images can be compensated effectively, achieving high quality InISAR 3D images consequently. [ABSTRACT FROM AUTHOR]

In forward‐looking imaging, left‐right Doppler is ambiguous and cross‐range resolution is limited by real aperture, which result in traditional synthetic aperture radar imaging methods no longer applicable. Besides, the impact of three‐dimensional acceleration on an over‐complete dictionary matrix and sparse reconstruction kernel is not considered in conventional super‐resolution forward‐looking imaging methods. To solve these problems, a super‐resolution forward‐looking imaging method for manoeuvering platforms is proposed. First, the range walk caused by forward‐looking geometry and higher‐order phase introduced by three‐dimensional acceleration are eliminated by compensating a pre‐processing function. Subsequently, according to beam steering‐based geometry and linear regression model, the over‐complete dictionary matrix is optimised to more accurately describe the complicated forward‐looking geometry with curved trajectory. Finally, sparsity adaptive matching pursuit kernel with an improved halting condition is adopted to reconstruct the forward‐looking scene. The proposed method is supported by point target simulation and surface target simulation. [ABSTRACT FROM AUTHOR]

Accurate range profile alignment is an essential step for achieving the subsequent azimuth coherent processing for inverse synthetic aperture radar image formation. In this study, an iterative method based on minimum entropy is first proposed for accurate range profile alignment, which constructs a series of local quadratic curves to gradually approach the extremum of the entropy of range profiles. The accuracy of range profile alignment depends on whether the iteration could be convergent to the optimal extremum. Unfortunately, the entropy of misaligned range profiles usually has numerous local extrema. The low-pass profile filtering is able to smooth the entropy surface and eliminate the local extrema. Nevertheless, the detailed features of range profiles are also lost due to the filtering so that the alignment accuracy could be reduced. Finally, a circulation cascade processing by appropriately combining the proposed iterative method and the low-pass filtering is presented to make the range profile alignment both convergent and accurate. Simulations and real data are used to validate the performance of the proposed method on iteration convergence and alignment accuracy. [ABSTRACT FROM AUTHOR]