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In joint radar-communications (JRC) applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver. The ill-posed problem of recovering all signal and channel parameters from the overlaid signal is termed as dual-blind deconvolution (DBD). In this work, we investigate DBD for a multi-antenna receiver. We model the radar and communications channels with a few (sparse) continuous-valued parameters such as time delays, Doppler velocities, and directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to minimize the sum of multivariate atomic norms (SoMAN) that depend on unknown parameters. To this end, we devise an exact semidefinite program using theories of positive hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of the number of radar targets and communications paths rather than their sum. We show that our approach is easily generalized to include several practical issues such as gain/phase errors and additive noise. Numerical experiments show the exact parameter recovery for different JRC scenarios. • Multi-antenna dual-blind deconvolution (M-DBD) is a general formulation for ISAC receiver. • Atomic norm minimization exploits sparsity to estimate the unknown continuous-valued variables. • Strong theoretical guarantees for exact recovery. • Generalization for practical scenarios with gain/phase errors in the receiver. [ABSTRACT FROM AUTHOR]

Most of the previous radar automatic target recognition (RATR) methods based on high resolution range profile (HRRP) are designed under the assumption of complete target-aspects, which assumes the HRRP samples with different target-aspects are complete in training dataset or template library. Few works were concentrated on HRRP RATR with incomplete target-aspects. However, it is extremely difficult and sometimes even impossible to obtain HRRPs with complete target-aspects in real world applications. Therefore, it is required to recognize targets of unseen target-aspects with an incomplete target-aspect template library. Aiming at this problem, a scattering center neural network (SCNet) is proposed for radar HRRP target recognition with incomplete target-aspects. Based on the assumption that an HRRP sample can be represented by a linear combination of a few atoms from a scattering center dictionary, we proposed a scattering center layer (SC-layer), which encapsulates the dictionary coding into an implicit layer and treats the scattering center dictionary as its parameter that is optimized in an end-to-end manner. We further proposed a discriminative target-aspect frame dictionary by dividing HRRPs of each class into multiple target-aspect frames and associating frame label information with each atom of it to enforce discriminability in features. A multiple target-aspect prototype classifier is proposed, which is more robust to intra-class variations and thus more suitable to handle the incomplete target-aspects problem compared with softmax classifier. The classification is simply implemented by matching HRRP features with each prototype and each HRRP is assigned to the class with the nearest prototype. In order to integrate both intra-class compactness and inter-class separation, we proposed a novel discriminative prototype loss. Taking the advantage of the discriminative prototype loss, HRRPs belonging to the same class and the same target-aspect frame are pulled closer to the corresponding prototype in the feature space, while simultaneously pushing apart from other prototypes of different classes. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset demonstrated the superior performance of the proposed method compared with other HRRP-based RTAR methods under the condition of incomplete target-aspects. • SCNet is based on that HRRP can be represented by a scattering center dictionary. • SC-layer is an implicit layer and treated scattering center dictionary as parameters. • Frame label is associated with atom of discriminative target-aspect frame dictionary. • Multiple target-aspect prototype classifier is more robust to intra-class variations. • Prototype loss integrated both intra-class compactness and inter-class separation. [ABSTRACT FROM AUTHOR]

• The proposed NRFRP in time dimension can accurately eliminate DFM with a onestep process of matrix multiplication, which greatly reduces the computational burden. • The introduced MSCFT in time dimension corrects RWM accurately without the Doppler ambiguity effect. Furthermore, due to the proper selection of scale factor, the DFM in space dimension can be omitted, and the search operation of the Doppler center can be avoided. • The proposed STHI algorithm can significantly improve the detection performance of maneuvering targets compared with the time dimension integration only. • The proposed algorithm can compensate for Doppler spectrum ambiguity, which improves the robustness of the algorithm effectively. Moving target detection in spaceborne distributed aperture radar (SBDAR) is a challenging task in civilian and military fields. The common issues are that the current methods suffer from the large range cell migration (RCM) and time-varying Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain, which is caused by distributed aperture radar detection configuration and the maneuvering characteristics of moving targets. To address these issues, a novel SBDAR moving target detection approach via space–time hybrid integration (STHI) is proposed in this paper. Compared with single or bistatic radar mode, the utilization of multiple satellites provides an improvement in moving target detection performance, together with the capabilities of localization and parameters estimation. The proposed method mainly includes the following three steps. First, the geometric and signal models of an observed maneuvering target are accurately established in a SBDAR framework, where the space-time 2D RCM and DFM are analyzed. Based on that, the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT) are developed to correct the RCM and DFM, achieving an efficient time dimension coherent integration for maneuvering target. After that, by dividing the radar detection area, the spatial projection method is adopted to integrate the moving target energy in space dimension. Finally, the simulation results verify the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• A new decision tree with preference is proposed to build a feature-based detector for small target detection in sea clutter. • Novel splitting and pruning processes are developed to meet the unbalanced demands of error probabilities and samples in radar target detection. • The proposed detector breaks the dimension limitation and has good performance and generalizability. It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors. [ABSTRACT FROM AUTHOR]

To improve the detectability of extended targets, a novel mismatched filter is designed, which deliberately broadens the 3 dB mainlobe width (MW) of the output response. Similar to how narrowband radar accumulates target energy, this filter concentrates the energy of scattering centers into the same resolution unit. To achieve this, constraints on 3 dB MW, mismatched filter gain, and signal-to-noise ratio (SNR) are imposed on the mismatched filter, while the integration sidelobe level (ISL) is minimized as the figure of merit. To handle the resulting non-convex problem, a polynomial-time method based on sequential convex approximation (SCA) which slacks the non-convex constraint is proposed to decrease ISL. Numerical results demonstrate successful broadening of the 3 dB MW within a certain range, and efficient accumulation of extended target energy. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, this is the first study to consider the azimuth estimation errors of the HRRP and relate them to deep neural network parameter control. • We proposed two new convolutional kernel parameterization methods, VPCONV and extended CVPCONV, which embed SI into deep network by using it to parameterize the convolutional kernel. This enables direct control of the network structure. Simultaneously, variational encoding is introduced in the reparameterization process to generalize the azimuth estimation error using a heteroscedastic Gaussian distribution with varying mean and variance. Notably, VPCONV and CVPCONV exhibit good transferability and can be used as plug-and-play modules. • Based on CVPCONV, we designed a lightweight network CVPCNN, which can make full use of the azimuth of the HRRP, decouple the tight coupling between the azimuth and target HRRPs, and extract discriminative characteristics that are more relevant to the current environment (azimuth). This helps obtain a more robust and accurate recognition performance. In addition, the CVPCNN introduces a sample-dependent kernel attention mechanism based on parallel convolution to achieve efficient inference, even with increased model complexity. • The effectiveness of the proposed model was tested in a target recognition task using a three-class measured aircraft HRRP dataset. The experiments show that the CVPCNN model, with just three convolutional layers, demonstrate optimal performance on this dataset. This represents a 4 % improvement over the standard CNN model under identical experimental conditions and model structure. Significantly, the CVPCNN maintain good recognition performance under non-ideal conditions, such as small sample and incomplete HRRP attitudes. Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems. [ABSTRACT FROM AUTHOR]