Owing to the large degrees of freedom and reduced mutual coupling by generating difference coarrays, nonuniform linear arrays have aroused great interest in direction of arrival estimation. Previous works have shown some improved sparse arrays, while few find the common features hidden within these structures. In this paper, we define a generic-coarray concept to reveal the impacts of variable ranges and element spacing on the uniform degrees of freedom (uDOFs), by which the sufficient condition for the connected coarrays is derived. We then propose an enhanced and generalized coprime array (EGCA) structure from the generic-coarray perspective. We show that the closed-form expression for the range of uDOFs is a function of sensor numbers and inter-element spacing. We prove that, by coarray extension and hole filling, the optimized EGCA possesses more uDOFs than the previous coprime arrays. Furthermore, EGCA also provides the minimum number of sensor pairs with small separation. Simulations verify the superiority of EGCA using the subspace-based algorithm.

In this paper, the convolution neural networks (CNN) are developed for the classification and localization of mixed near-field and far-field sources by using the geometry of symmetric nested array. We first transform the received data into frequency domain. Then, we preprocess the phase difference matrix to decouple mixed sources. Considering that the counter-diagonal elements in the phase difference matrix only contain the direction of arrival (DOA) parameter of each mixed source, we utilize the upper right elements as the input of CNN to estimate the DOA of each mixed source. In order to avoid the influence of noise, we construct a particular range vector without the estimated DOA and employ the output of autoencoder to classify the mixed sources. Finally, we further exploit the output of autoencoder and apply the CNN without bias vectors to estimate the range of near-field sources. In contrast to the traditional learning-based approaches regarding the parameter estimation as a classification problem, the proposed approach considers the parameter estimation as a regression problem and can significantly reduce the complexity of networks. Simulation results demonstrate that the proposed learning-based method can improve the precision for the mixed source localization. Moreover, the proposed method is robust to the off-grid parameters and the different numbers of mixed sources.

Recently, deep unfolded networks have been widely utilized in direction of arrival (DOA) estimation due to the reduced computational complexity and improved estimation accuracy. However, few consider the nested array for off-grid DOA estimation, where the estimated DOAs are not on the prespecified grids. In this paper, we propose a deep unfolded FOCal Underdetermined System Solver (FOCUSS) network and a deep unfolded Alternating Direction Method of Multiplies (ADMM) to address the problem, which respectively aim to improve estimation accuracy and further reduce computational complexity. We first apply first-order Taylor expansion and vectorize the covariance matrix into a real-valued single snapshot for network input. We then train the proposed networks to obtain on-grid DOA spatial spectrum and off-grid values, where the off-grid DOA estimation is calculated by the peaks of on-grid DOA spatial spectrum and corresponding off-grid values. We demonstrate that the proposed networks with interpretable parameters can accelerate the convergence rate and achieve better generalization. Simulations verify the performance of proposed networks in comparison with the existing methods.

Deep learning methods have played a more important role in hyperspectral image classification. However, general deep learning methods mainly take advantage of the samplewise information to formulate the training loss while ignoring the intrinsic data structure of each class. Due to the high spectral dimension and great redundancy between different spectral channels in the hyperspectral image, these former training losses usually cannot work so well for the deep representation of the image. To tackle this problem, this work develops a novel deep manifold embedding method (DMEM) for deep learning in hyperspectral image classification. First, each class in the image is modeled as a specific nonlinear manifold, and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several subclasses. Finally, considering the distribution of each subclass and the correlation between different subclasses under data manifold, DMEM is constructed as the novel training loss to incorporate the special classwise information in the training process and obtain discriminative representation for the hyperspectral image. Experiments over four real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method when compared with general sample-based losses and showed superiority when compared with state-of-the-art methods.

Time-coding digital metasurface (TCDM) has attracted extensive attention in radar deception jamming due to its ability to adjust the reflected electromagnetic waves flexibly. However, wideband imaging radar can obtain the complex structural features of targets through the high-resolution range profile (HRRP), thus identifying simple scattering point deception jamming. In this paper, a method of target HRRP deception imitation is proposed based on TCDM. The non-periodic time modulation model of TCDM is derived and it is theoretically proved that the complex distribution of scattering points in HRRP can be accurately imitated by a single TCDM with a specific non-periodic sequence. Comparisons with existing schemes reveal that the proposed solution combines the advantages of flexibility, realism, operability, and resource efficiency. Both simulation and experiment results show that the cosine similarity between real and imitated HRRP peaks can exceed 90% in various scenarios, which verifies the robustness and practicality of the proposed method.

Recently, deep unfolding networks with interpretable parameters have been widely utilized in direction of arrival (DOA) estimation due to the faster convergence speed and better generalization ability. However, few consider the nested array for gridless DOA estimation. In this letter, we propose a deep alternating projection network to address the problem. We first convert the covariance matrix into a measurement vector in the form of atomic norm, which can reduce the matrix dimension during projection. We then train the proposed network to alternately obtain the positive semi-definite matrix and the corresponding irregular Hermitian Toeplitz matrix, where the loss function is derived by employing the trace of network output. Finally, we apply the irregular root Multiple Signal Classification (MUSIC) method to obtain gridless DOA via nested array. We demonstrate that the proposed networks can accelerate the convergence rate and reduce computational cost. Simulations verify the performance of proposed networks in comparison with the existing methods.