Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.

Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation. This paper proposes two deep unfolded gridless DOA estimation networks to resolve the above problem. We first consider the atomic norm-based 1D and decoupled atomic norm-based 2D gridless DOA models solved by the alternating iterative minimization of variables, respectively. Then, the corresponding deep networks are trained offline after constructing the corresponding complete training datasets. At last, the trained networks are applied to realize the 1D DOA and 2D estimation, respectively. Simulation results reveal that the proposed networks can secure higher 1D and 2D DOA estimation performances while maintaining a lower computational expenditure than typical methods.

A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time radar data cube (RDC). We build scattering center models of precession cone-shaped targets with the occlusion effect. The Binary Mask method is first utilized to obtain a high-resolution range-Doppler (RD) sequence. On this basis, the range–frequency–time RDC can be extracted from the RD sequence. In order to approach the actual case, we discuss the parameter estimation algorithm under different radar lines-of-sight (LOS). The most attractive attribute of this algorithm is that it can conduct in-depth research on m-D parameter estimation from a three-dimensional (3D) domain. Finally, the experimental results illustrate the effectiveness of the proposed method.

In inverse synthetic-aperture radar (ISAR) imaging, it is essential to deal with the Doppler ambiguity of group targets with complex maneuvers in order to avoid the bias of target position towards the actual value. Simultaneously, migration through resolution cell (MTRC) under the Doppler ambiguity is unable to be compensated for as a preprocessing. Traditional ISAR imaging methods for maneuvering targets, however, are undesirable to handle the severe deformation and defocusing in the imaging results induced by the Doppler ambiguity and MTRC. In this paper, we propose a novel and effective ISAR imaging method to improve the imaging quality by removing the Doppler ambiguity and compensating for the MTRC. Specifically, we first model the echo as a multi-component cubic phase signal (m-CPS) and design a high-order instantaneous autocorrelation function–generalized scaled Fourier transform (HIAF–GSCFT) to process the echo. This is to estimate the rotational parameters without MTRC compensation. Then, a maximum weighted contrast algorithm is used to remove the Doppler ambiguity, followed by reconstructing the echo. Compared with the existing method, the proposed method can accurately estimate the rotational parameters under the existing MTRCs and achieves a high-quality ISAR image for group targets, with complex maneuvers without Doppler ambiguity. Experiments of simulated and measured datasets validate its effectiveness and robustness for single target and group targets.

Ship detection of synthetic aperture radar (SAR) images holds significant value for both civilian and military applications. Compared to horizontal ship detection, oriented ship detection based on oriented bounding boxes can capture the orientation and aspect ratio of ships, thus receiving increasing attention. However, in oriented ship detection, when ships rotate near the specific angles, the angle prediction result obtained by the deep learning network may have a severe mutation, which is the well-known boundary discontinuity problem. To address the issue of boundary discontinuity in SAR ship detection, researchers have proposed numerous methods. However, through our systematic analysis, we found that these methods do not fundamentally solve the problem. To this end, we first clarified the reasons for the existence of boundary discontinuity and how it affects the detection network. Based on this, we proposed the conditions that the encoding methods and loss functions of the detection network must satisfy to address the issue of boundary discontinuity. In line with these conditions, we designed a continuous encoding method called coordinate decomposition method (CDM). In addition, we also analyzed the impact of different optimization methods on the detection network and, based on this, presented a joint optimization paradigm based on continuous encoding. Experimental results on two commonly used SAR ship detection datasets demonstrate that our proposed CDM encoding method effectively addresses the boundary discontinuity issue and enhances the detection performance. Compared to the state-of-the-art methods, the fully convolutional one-stage network using the CDM-based joint optimization achieves optimal detection results without employing any additional techniques.

Inverse synthetic aperture radar (ISAR) images have become one of the most important pieces of information for airborne and maritime target identification. In general, ISAR images with higher resolution and lower background noise provide more precise target information, thus improving target identification accuracy. However, upgrading the resolution of the ISAR system is costly. Super-resolution algorithms that can utilize low-resolution echoes to obtain high-resolution imaging results have become an important means of improving ISAR imaging resolution. The traditional ISAR super-resolution imaging technique suffers from high side lobes and wide main lobes. In addition, de-noising algorithms based on filtering operators tend to lead to image blurring. This work proposes a deep network for super-resolution and de-noising of ISAR images called SR-DNnet. Specifically, we view super-resolution and de-noising as a series of up-sampling, two-dimensional filtering, and threshold shrinkage. These operations are exactly what deep networks are good at. SR-DNnet has 15 layers, enabling 4x super-resolution and de-noising of ISAR images. The parameter scale of SR-DNnet is much smaller than most deep networks, which makes it efficient to train. The SR-DNnet we built features complex-value inputs, residual learning, multipath learning, and progressive up-sampling. A series of simulated and measured dataset experiments prove that the SR-DNnet is efficient and well-performed on super-resolution and de-noising.