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Hybrid MIMO phased array radar (HMPAR) is widely recognized in the research of radar systems owing to its capacity to provide a balance or trade‐off between transmit coherent gain and waveform diversity gain. The balance or trade‐off between these two gains is closely related to spatial coherence between different arrays, represented by the degree of cross‐correlation between steering vectors over different angles. Proper subarray partitions can provide an extra degree of freedom for optimising the coherence of the array manifold. A coherence optimised subarray partition for HMPAR is proposed with an analysis of connections between coherence and subarray partitions. First, the definition of coherence of array manifold is presented, and its connection between beampattern and the signal to interference plus noise ratio (SINR) is analysed. Next, the problem of optimising array manifold coherence is transformed into the discrete coherence optimization of the steering vector codebook. By properly selecting different codewords with the proper degree of correlation between each other, coherence between the target and interference steering vectors is optimised. The original combinatorial problem is approximated by a series of subproblems, which is solved in iteratively. Numerical results revealed that the proposed subarray partition provides lower coherence, lower beampattern peak sidelobes and higher SINR compared with` other subarray configurations.

Conventional transmit subarray partitioning schemes of phased-MIMO radar will cause several problems, the reduction of array aperture, the increase of feeding network complexity and optimization algorithms time cost. Focus on the problems above, this paper proposes a deep learning-based interleaved sparse transmit subarray partitioning method. Firstly, the training data is generated by phased-MIMO array manifold matrix. Secondly, a dimensionality reduction method for radar data is introduced to reduce the dimensionality of the sample data while minimizing information loss. Then, a lightweight convolutional neural network is constructed for training and the optimal array structures is selected by classification. Finally, linear and plane arrays experiment results show that our proposed method can achieve 97.95% classification accuracy, better than other conventional dimensionality reduction methods and neural networks; compared with the traditional SCP partitioning method, our proposed method has similar beampattern sidelobe level and DOA estimation accuracy, but the time cost is greatly reduced.

By transmitting multiple independent waveforms at the transmit side and processing echoes of spatial targets at the receive side, Multiple Input Multiple Output (MIMO) radar enjoys virtual array aperture expansion and more degree of freedom (DOF), both of which favors the application of direction finding or estimation of direction of arrival (DOA). The expanded virtual aperture provides higher angular resolution which also promotes the precision of DOA estimation, and the extra DOF brought by waveform diversity can be leveraged to focus energy in certain spatial region for better direction-finding capacity. However, beamspace methods which match certain beampatterns suffer from deteriorated performance and complexity in implementation, and the advantage of virtual array aperture is limited by its virtual element redundancy. As an important performance indicator of DOA estimation, Cramer–Rao Bound (CRB) is closely connected to the array configuration of the system. To reduce the complexity of the system and improve CRB performance at the same time, in this paper, the virtual array of MIMO radar is designed directly by selecting outputs from matched filters at the receive side. For the sake of fair comparison, both scenarios with and without priori directions are considered to obtain optimized virtual array configuration, respectively. The original combinatorial problems are approximated by sequential convex approximations methods which produce solutions with efficiency. Numerical results demonstrate that the proposed method can provide thinned virtual arrays with excellent CRB performance.

An innovative approach based on the two-dimensional unitary matrix pencil (2-D UMP) is proposed for the synthesis of arbitrary sparse planar arrays. In this study, the estimation of array parameters is recast as a 2-D poles extraction problem, and the number of antenna elements is minimized by the reduced rank processing. Firstly, the 2-D element positions including horizontal and vertical coordinates are directly found as the generalized eigenvalues of the equivalent matrix pencils without solving poles. Then the element excitations can be computed with the least-squares estimator once the real positions are obtained by the correct pairing procedure. The pattern matching accuracy and the array sparseness are further improved owing to the utilization of a unitary transformation, which converts a complex matrix to a real one along with its eigenvectors. A set of representative numerical experiments is provided to assess the effectiveness and advantages of the proposed method.

An innovative technique based on the enhanced unitary matrix pencil (MP) method is presented for the design of sparse multiple-pattern linear arrays. By virtue of the equivalent MP obtained with a unitary transformation, the relation between the element positions and the generalized eigenvalues is achieved in this method, which contributes to the real solutions of the common element positions for all multiple patterns. Owing to the utilization of a unitary transformation that can convert a complex matrix to a real one, the computational complexity can be significantly reduced since only the real computations are involved in the singular value decomposition and eigenvalue decomposition procedures. Consequently, by only varying the obtained excitation distributions, different patterns are generated with a higher matching accuracy. Representative experiments are provided to validate the effectiveness and advantages of the proposed method.

In this letter, the unitary matrix pencil (UMP) method is utilized for the shaped-beam pattern synthesis of maximally sparse linear arrays. Taking advantage of the equivalent matrix pencil obtained by a unitary transformation, the UMP-based synthesis method constructs a novel relation between the element positions and the generalized eigenvalues, which contributes to the real solutions for all element positions and thereby improves the matching accuracy of shaped-beam patterns. In addition, complex computations in both the singular value decomposition and the eigenvalue decomposition procedures are converted into real ones through a unitary transformation, which significantly reduces the computational complexity. Numerical experiments assess the effectiveness and advantages of the proposed method in comparison to the state-of-the-art synthesis approaches.

The synthesis of pattern reconfigurable antenna array with as few elements as possible can find wide applications in radar tracking, biomedical imaging, satellite and ground communications, and remote sensing applications. In this study, an efficient method for multi-task learning (MTL) is exploited to the design of sparse pattern reconfigurable antenna array. Toward this end, the design of sparse and pattern reconfigurable antenna array is reformulated as an equivalent problem of multi-matrices linear regression and iterative shrinkage threshold method for MTL is utilised to obtain jointly optimal design of positions and the excitations of the radiating elements for multi-pattern synthesis, in which compromise between the array sparseness and pattern matching is achieved. Some numerical simulations are presented to assess the efficiency of the proposed method and the synthesis performance comparisons with matrix pencil method and genetic algorithm are also performed to demonstrate the superiority of the proposed algorithm over traditional algorithm in this literature.

According to the newly proposed nested MIMO (Multiple-Input Multiple-Input Multiple Output Multiple Array) array design method, we propose to replace the traditional nested array into an optimizing nested array, ie, to optimizing nested MIMO array design. It not only retains the original advantage of nested MIMO array design closed expression with array element position and degree of freedom(DOF), but also greatly improves the array aperture and DOF. Optimizing nested MIMO array firstly uses the optimizing nested array as the transmitting and receiving arrays, and then make the difference set processing for the coarray of MIMO array (coarray, CA). By properly designing the array spacing of the transmitting and receiving arrays, we can obtain a non-porous difference array. When the total number of array elements is given, by analyzing the characteristics of the array structure, the best array element number of the transmitting and receiving arrays can be obtained. Simulation experiments show that compared with the nested MIMO array design, the proposed method can effectively expand the array aperture, increase the DOF, and increase the DOA estimation accuracy of the MIMO radar without increasing the number of actual array elements.

Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm in the second step. In the third step, the approximated system matrix is applied for the design of sparse attack vector based on the convex optimization. The simulation results show that the EID attack strategy achieves a better performance than the improved ICA-based attack strategy in constructing perfect sparse attack vectors. What is more, data-driven implementation of the proposed strategy is also presented which ensures attack performance even without the prior information of the system.

In view of the fact that the current online virtual network embedding algorithms do not consider the fragment resources generated in the embedding process deeply enough, resulting in the problem that the acceptance ratio and the revenue to cost ratio are both low, a mathematical model for virtual network reconfiguration is constructed and a heuristic algorithm for fragment-aware virtual network reconfiguration (FA-VNR) is proposed. The FA-VNR algorithm selects the set of virtual nodes to be migrated according to the fragment degrees of the physical nodes, and selects the best virtual node migration scheme according to the reduction of the fragment degrees of the physical nodes as well as the reduction of the embedding cost of the embedded virtual networks. Extensive simulation results show that the proposed FA-VNR algorithm not only can obviously improve the acceptance ratio and the revenue to cost ratio of the current online virtual network embedding algorithm, but also has better optimization effect than the existing virtual network reconfiguration algorithm.

Stochastic Context-Free Grammars (SCFG) have promising application prospect in the field of Multi-Function Radars (MFR) states recognition and threat estimation, which entails the fast learning of the probability of radar grammar production based on training data. Conventional learning algorithms are limited in practical application for their high computational complexity. A new fast learning algorithm for the probability of MFR grammar is proposed in this paper in light of a reformulated and delicate SCGF modeling of the MFR signal generation mechanism. The proposed algorithm first pre-compute Cocke-Younger-Kasami(CKY) parsing chart for each training sequence, and then the probability of radar grammar production is estimated with modified Inside-Outside(IO) algorithm based on the aforementioned parsing chart. The computational complexity and accuracy of the algorithm are also analyzed in detail. Simulation results show that the algorithm could fast estimate production rule probabilities with favorable estimation accuracy, and compared with the conventional IO or Viterbi Score (VS) algorithm, more than a half operation time can be reduced.

In multiuser MIMO downlink systems, the block diagonalization (BD) precoder suffers from two major drawbacks: the dimensionality constraint on antennas arrays and the inability in handling spatially correlated users. User-selection capable of selecting only the most sum-rate-contributing users is a promising measure to alleviate this situation. One low complexity sub-optimal user selection algorithms is proposed for multiuser MIMO downlink systems with multiantenna users and blocking diagonalization (BD) precoding. It is shown to possess superior sum-rate performance than the existing Frobenius-norm-based algorithm due to the additional consideration of the inner-user channel correlation and the high-rank inter-user channel correlation. Different from the existing Frobenius-norm-based sub-optimal user selection algorithm, the matrix volume of the aggregate channel matrix of the selected users is adopted in our proposed algorithms as the selection criterion. The proposed algorithm, known as the ‘global-max-volume algorithm’, aims to globally search the maximum-volume aggregate channel matrix and acquires a improved sum-rate performance over the existing Frobenius-norm-based algorithms. Fast algorithms to speed up the computation process of the proposed algorithms are also introduced.

Array calibration with angularly dependent gain and phase uncertainties has long been a difficult problem. Although many array calibration methods have been reported extensively in the literature, they almost all assumed an angularly independent model for array uncertainties. Few calibration methods have been developed for the angularly dependent array uncertainties. A novel and efficient auto-calibration method for angularly dependent gain and phase uncertainties is proposed in this paper, which is called ISM (Instrumental Sensors Method). With the help of a few well-calibrated instrumental sensors, the ISM is able to achieve favorable and unambiguous direction-of-arrivals (DOAs) estimate and the corresponding angularly dependent gain and phase estimate simultaneously, even in the case of multiple non-disjoint sources. Since the mutual coupling and sensor position errors can all be described as angularly dependent gain/phase uncertainties, the ISM proposed still works in the presence of a combination of all these array perturbations. The ISM can be applied to arbitrary array geometries including linear arrays. The ISM is computationally efficient and requires only one-dimensional search, with no high-dimensional nonlinear search and convergence burden involved. Besides, no small error assumption is made, which is always an essential prerequisite for many existing array calibration techniques. The estimation performance of the ISM is analyzed theoretically and simulation results are provided to demonstrate the effectiveness and behavior of the proposed ISM.

The presence of unknown mutual coupling between array elements is known to significantly degrade the performance of most high-resolution direction of arrival (DOA) estimation algorithms. In this paper, a robust subspace-based DOA estimation and array auto-calibration algorithm is proposed for uniformly linear array (ULA), when the array mutual coupling is present. Based on a banded symmetric Toeplitz matrix model for the mutual coupling of ULA, the algorithm provides an accurate and high-resolution DOA estimate without any knowledge of the array mutual couplings. Moreover, a favorable estimate of mutual coupling matrix can also be achieved simultaneously for array auto-calibration. The algorithm is realized just via one-dimensional search or polynomial rooting, with no multidimensional nonlinear search or convergence burden involved. The problem of parameter ambiguity, statistically consistence and efficiency of the new estimator are also analyzed. Monte-Carlo simulation results are also provided to demonstrate the effectiveness and behavior of the proposed algorithm.