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Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.

Synthetic aperture radar (SAR) target classification methods based on convolutional neural networks (CNNs) are susceptible to overfitting due to limited samples. In addition, the position and deformation variations of SAR targets can also affect the feature extraction capabilities of CNN. To address these issues, this article proposes a novel few-shot learning (FSL) method, called position-aware graph neural network (PA-GNN). The PA-GNN introduces a PAM to enhance the feature representation of the target. Specifically, deformable convolution is incorporated into the embedding network to accommodate the various shapes of SAR targets. Then, the self-attention network is brought in to capture the spatial dependence of any two positions in the feature maps, and the cross-attention network calculates the cross-attention between support and query feature maps to handle the problem of unseen categories. These two attention networks can be exploited to highlight the target regions. Besides, to integrate the features extracted by these two attention networks, an adaptive feature fusion module is designed to obtain discriminative features of the target position. The PA-GNN conducts measurement by constructing multiple layers of GNN and utilizes residual learning to alleviate oversmoothing. Furthermore, we introduce a fused loss to enhance the separability between different categories and the similarity within the same category. Experiments on OpenSARShip and MSTAR datasets demonstrate that the proposed PA-GNN can achieve comparable classification results compared with other FSL algorithms.

For multichannel synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, the clutter suppression method based on post-Doppler space time adaptive processing technology is popular due to its excellent performance. However, to coherently integrate a target's energy of each channel, this kind of method requires a search of target's velocity, which would introduce a large computation load when the search step size is too small. While the existing studies mainly focus on improving its performance in the cases of complex scenes and geometries, the studies on optimizing the search step size are rare. To address this issue, this article proposes a method for calculating the optimal velocity search step size of adaptive clutter suppression for the commonly used airborne three-channel SAR-GMTI systems. First, the signal models in the range Doppler domain are developed. Then, considering that both the signal-to-clutter-noise ratio loss of clutter suppression and the coherent integration gain loss are introduced by the mismatch of target's steer vector, the relationship between the coherent accumulation gain loss and the velocity search error is utilized to develop the analytical expression for the optimal velocity search step size. Experimental results validate the proposed method.

Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.

Airborne forward-looking radar (AFLR) has been more and more impoatant due to its wide application in the military and civilian fields, such as automatic driving, sea surveillance, airport surveillance and guidance. Recently, sparse deconvolution technique has been paid much attention in AFLR. However, the azimuth resolution performance gradually decreases with the complexity of the imaging scene. In this paper, a data-driven airborne Bayesian forward-looking superresolution imaging algorithm based on generalized gaussian distribution (GGD- Bayesian) for complex imaging scene is proposed. The generalized gaussian distribution is utilized to describe the sparsity information of the imaging scene, which is quite essential to adaptively fit different imaging scenes. Moreover, the mathematical model for forward-looking imaging was established under the maximum a posteriori (MAP) criterion based on the Bayesian framework. To solve the above optimization problem, quasi-Newton algorithm is derived and used. The main contribution of the paper is the automatic selection for the sparsity parameter in the process of forward-looking imaging. The performance assessment with simulated data has demonstrated the effectiveness of our proposed GGD- Bayesian algorithm under complex scenarios.

This paper deals with adaptive moving target detection for a forward scatter radar in complex Gaussian noise. The echoes received by the forward scatter radar include not only the noise and the possible target signals but also the direct signals. To suppress the direct signals and detect the target signals, Rao and Wald tests are derived in two cases: the secondary data which contain no target signal are available or not available. Different from monostatic radar, it is proved that the derived Rao and Wald detectors for the forward scatter radar have the same test statistics as the generalized likelihood ratio test-based detector in the complex Gaussian noise both when the secondary data are available or not available. The numerical evaluation further demonstrates the equivalence and the effectiveness of the proposed detectors.

The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian noise whose covariance matrix is unknown. To improve robustness to mismatched signals, a random perturbation that follows the complex normal distribution is added under the alternative hypothesis. Since traditional detectors that divide complex parameters into real parts and imaginary parts are sometimes difficult to obtain, a new robust, complex parameter gradient test is derived directly from the complex data. Moreover, the CFAR property of the new detector is proven. The performance assessment indicates that the gradient detector exhibits suitable robustness to the mismatched signals.

Airborne circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems are attractive tools for air-to-ground surveillance and monitoring, because of their advantages of short revisit time and large coverage. This paper proposes an accurate imaging and Doppler chirp rate estimation algorithm for the airborne CSSAR-GMTI system with high range resolution. A key step of this algorithm is to utilize an alternation strategy to implement the correction of the target's range cell migration and the estimation of the range equation's quadratic coefficient alternatively. This step enables the proposed algorithm to achieve an accurate imaging and Doppler chirp rate estimation of a ground moving target. In addition, this paper presents an investigation on the accuracy of the quadratic-approximated range equation for ground moving target imaging. Numerical experiments are conducted to validate the proposed algorithm.

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in a known subspace, which is independent with the target signal subspace. By resorting to the two-step generalized likelihood ratio test, two-step Rao, and two-step Wald design criteria, three adaptive subspace signal detectors are proposed. Moreover, the constant false-alarm rate property of the proposed detectors is proved. The experimental results based on IPIX real sea clutter data and simulated data illustrate that the proposed detectors outperform their counterparts.

Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target.

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

This letter addresses the problem of target relocation in wide-area ground moving target indication system. The antenna pattern information is exploited as prior knowledge to enhance the performance of airborne radar. Firstly, the ideal target relocation model for moving targets based on the dual-channel radar system is analysed and the mismatch model of the receiving channel is constructed. Then the reason for poor relocation performance is analysed and the channel error model is constructed. Based on this fact, the problem of target relocation is converted into a problem of channel phase estimation. Moreover, a least-squares method is utilised to estimate the phase error by exploiting the real antenna pattern information and a modified mono-pulse curve (MPC) is derived. Finally, the real geometry location of the moving target can be achieved with the modified MPC. Airborne radar experiments are given to verify the effectiveness of the proposed algorithm.

This study deals with the problem of adaptive detection in the presence of mismatch signals in partially homogeneous environment. To enhance the selectivity of the detector, the authors add a fictitious signal under the null hypothesis. The fictitious signal is orthogonal to the nominal signal steering vector in the quasi-whitened observation space. Two generalised likelihood ratio test-based mismatch selective detectors are derived. Simulation results show that the one-step mismatch selective detector coincides with the adaptive coherence estimator and the two-step mismatch selective detector has a better mismatch rejection capability than the generalised adaptive matched filter.

The problem of real-time imaging in synthetic aperture radar (SAR) system is addressed here. Sub-aperture processing algorithm is usually used in the engineering applications. However, high azimuth sidelobes and edge aliasing problem usually occur in conventional sub-aperture algorithm, which degenerates the quality of the SAR image. To solve this problem, the reason for high azimuth sidelobes and edge aliasing problem is analysed, and a modified sub-aperture processing algorithm is proposed. The proposed algorithm minimises the effect of the phase discontinuity to improve the SAR image quality. Therefore, the azimuth sidelobes can be improved, and the edge aliasing phenomenon will be eliminated. Real measured airborne SAR datasets demonstrate the effectiveness of the proposed algorithm.

In order to solve synthetic aperture radar image-focus problem when space-variant motion errors could not be ignored, this study proposes a partitioned phase gradient autofocus (PGA) method. In this method, it is most important whether the dominant scatters is in the partitioned images or not. In order to find the dominant scatters, energy gradient factor is defined and energy inflection point is determined. Then, the judgment is made by energy gradient factor and its threshold. Based on measured data processing, it is indicated that the method could distinguish the partitioned images with significant dominant scatters, un-significant dominant scatters, and without dominant scatters. PGA is implemented on all blocks with significant dominant scatters and partial blocks with un-significant dominant scatters. There is no need to use PGA for the blocks without dominant scatters.

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.