The conventional direction-of-arrival (DoA) estimation approaches only be effective when the line-of-sight (LoS) link exists, while in the case of the non-line-of-sight (NLoS) situation, the spatial angle can not be captured and thus the DoA estimation performance would be significantly degraded. To address this challenge, a novel reconfigurable intelligent surface (RIS)- enabled gridless DoA estimation approach is proposed, where the RIS can establish the virtual LoS link between the base station (BS) and the targets. For extracting the statistics of the signal, the RIS-enabled signal model in the covariance domain is proposed. Then we estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to obtain the RIS-enabled covariance matrix free of noise nuisance. Additionally, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem. To ease the calculation burden, an efficient iterative approach finally is designed to solve the ANM problem via the alternating direction method of multipliers (ADMM). Numerical experiments validate the robustness of the proposed method against the benchmark in terms of computational efficiency and multi-source DoA estimation precision.

An off-grid direction-of-arrival (DOA) estimation method based on block sparse representation is proposed to localize the strictly noncircular (NC) sources utilizing an extended transformed nested array (ETNA). This novel off-grid DOA estimation algorithm effectively promotes spatial distribution information mining. Furthermore, it is conducive to providing stable signal recovery, which refines the DOA estimation precision with interpolation over a coarse grid. We then combine the above algorithm with the designed ETNA to improve the detection performance. The ETNA is an optimal displacement on the existing TNA, which enlarges the degree of freedom (DOF) and lengthens the maximum contiguous segment from the derived virtual array. Simulation results demonstrate its superiority in estimation performance and DOF.

To reduce the adverse impacts of the unknown colored noise on the performance degradation of the direction-of-arrival (DOA) estimation, we propose a new gridless DOA estimation method based on fourth-order cumulant (FOC)in this letter. We first introduce the non-redundancy single measurement vector (SMV) through FOC, which is capable of suppressing the Gaussian colored noise. Next, we analyze the distribution of the estimation error and design an estimation error tolerance scheme for it. We then combine the atomic norm minimization of the non-redundancy SMV with the above constraint scheme. This combination poses the stability of the sparsest solution. Finally, the DOA estimation is retrieved through rotational invariance techniques. Moreover, this method extends the gridless DOA estimation to the sparse linear array. Numerical simulations validate the effectiveness of the proposed method.

Microwave staring correlated imaging (MSCI) is an imaging method that forms a temporal-spatial stochastic radiation field (TSSRF) and correlates the echo with the known stochastic radiation field to obtain target scattering information. The randomness of TSSRF is the necessary condition for MSCI, and analyzing the spatio-temporal randomness of correlated imaging is the basis for optimizing radar parameters. This paper first introduces the multiple-input multiple-output (MIMO) system into radar correlation imaging for increasing radar detection channels. Then we construct the spatial ambiguity function of MIMO to analyze the randomness of the radiation field in MIMO MSCI. Finally, we analyze the influence of the receiving array geometry and different compounding methods of the multi-channel signal on the randomness of the radiation field and the imaging quality. Simulation experiments verify the relationship between the space-time randomness in MIMO MSCI and transceiver aperture and the compound of the received signals.

This study deals with the problem of mainlobe jam-ming suppression for rotated array radar. The interference becomes spatially nonstationary while the radar array rotates, which causes the mismatch between the weight and the snap-shots and thus the loss of target signal to noise ratio (SNR) of pulse compression. In this paper, we explore the spatial diver-gence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal (RAMS) model firstly. Then the corresponding algorithm improved blind source separation (BSS) using the fre-quency domain of robust principal component analysis (FD-RPCA-BSS) is proposed based on the established rotating model. It can eliminate the influence of the rotating parts and address the problem of loss of SNR . Finally, the measured peak-to-average power ratio (PAPR) of each separated channel is per-formed to identify the target echo channel among the separated channels. Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.

A gridless direction of arrival (DOA) estimation method for the two-dimensional (2-D) minimum redundant matrix (MRA) in nonuniform noise is proposed. First, a positive semi-definite programming model for restoring covariance matrix in 2- D MRA is established, and the denoising problem is transformed into a noise-free covariance matrix recovery problem. Second, the 2-D angle estimation is obtained by the unitary esprit method. This method can suppress the influence of nonuniform noise and has high estimation accuracy. Besides, it can also realize the automatic matching of azimuth and elevation angles. Simulations and experiments comparison confirm the performance of the proposed method.

Basis mismatch challenges the conventional direction-of-arrival estimation constrained by sparse representation, especially in the case of massive antennas limited to a single snapshot. In this letter, we develop a real-valued gridless direction-of-arrival estimation method to improve angular precision in the aforementioned circumstances. A new data model is first established through real-valued transformation and then estimate the large low-rank matrix with nuclear norm minimization. A fast iterative algorithm is designed to recover the underlying matrix faithfully and efficiently by the alternating direction method of multipliers. Numerical examples validate the performance improvement of the proposed method in the massive uniform linear array. This work also shows the potential to apply in measured data of the millimeter-wave multiple-input multiple-output system.

The most existing coprime array interpolation-based algorithms have the potential to increase the degree of freedom for direction-of-arrival (DOA) estimation. However, these algorithms are modeled on the Gaussian white noise and do not consider the possibility of nonuniform noise. To eliminate the unknown nonuniform noise, we propose a robust coprime array interpolation method in this paper. First, the vectorization is performed on the covariance matrix to obtain the difference co-array with overlapping sensors. Next, we develop a robust interpolation technique to optimize the signal model of the nonuniform virtual array and fill in the holes to convert it into a contiguous virtual uniform linear array (ULA). We then divide the virtual ULA into overlapping subarrays and reconstruct the Toeplitz covariance matrix through the denoising constraint. This constraint can alleviate the impact of nonuniform noise to ensure the robustness of the reconstruction. Finally, the DOA estimation is resolved through the MUSIC algorithm. Numerical experiments validate the superiority of the proposed algorithm.

High resolution range profile (HRRP) target recognition based on deep learning methods is mainly dedicated to changing the 2-Dimensional (2-D) convolutional neural network (CNN) framework into a 1-Dimensional (1-D) feature extractor. In this paper, a new algorithm called triple gramian angular field with CNN (TGAF-CNN) is proposed for HRRP recognition in the low signal-to-noise (SNR) condition. Di...

An off-grid sparse direction-of-arrival (DOA) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spatial coloured noise. The authors start by developing an off-grid sparse model based on linear interpolation with reweighted coefficient, which is a trade-off between tangent and secant offset, to guarantee an optimal approximation for off-grid signals. Next, the authors formulate the DOA estimation problem as solving the off-grid sparse model and, finally, the off-grid sparse model is addressed under the general framework of sparse Bayesian learning (SBL). Additional noise in IRLI is spatially coloured for calculating its statistical properties, which is different from SBL relying on the spatial white noise assumption. Numerical results with the limited snapshots and the low signal-to-noise ratio validate the algorithm by comparing with other algorithms.

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

With the improvement of radar resolution, the dimension of the high resolution range profile (HRRP) has increased. In order to solve the small sample problem caused by the increase of HRRP dimension, an algorithm based on kernel joint discriminant analysis (KJDA) is proposed. Compared with the traditional feature extraction methods, KJDA possesses stronger discriminative ability in the kernel feature space. K-nearest neighbor (KNN) and kernel support vector machine (KSVM) are applied as feature classifiers to verify the classification effect. Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality, and improve target recognition performance.

由于高分辨距离像(HRRP)具有便于获取、处理方便的优势,基于HRRP的雷达目标识别技术一直是雷达自动目标识别技术研究的热点.HRRP的几何结构特征能够直接反映目标的物理结构,在利用帧内最大相似像缓和HRRP的方位敏感性的基础上,HRRP的等效散射中心维数、等效目标尺寸等几何结构特征的提取,有效实现了三类不同飞机目标的识别.实验表明,在基于几何结构特征的HRRP目标识别中,与平均向量的帧中心提取方法相比,帧内最大相似像具有更好的识别效果.

提出了一种基于希尔伯特黄变换(HHT)的雷达高分辨距离像(HRRP)目标识别方法.首先,为了解决HRRP强度敏感性和平移敏感性问题,对原始HRRP采取预处理操作,从而得到经相关对齐后的归一化HRRP;然后对经相关对齐后的归一化HRRP进行HHT,得到Hilbert谱特征;最后采用字典学习模型对不同目标的Hilbert谱特征实现目标识别决策.通过处理雷达实测数据对所提算法进行验证,结果显示对3类飞机目标平均识别精度达到了93.55％,体现了该算法对雷达HRRP目标的良好识别性能.

The dictionary learning model can really reflect radar high resolution range profile(HRRP)po-tential structural characteristics and the statistical modeling algorithm can effectively solve the HRRP attitude sensitivity problem.Based on those features,researches on the selection of atoms and the discriminant optimiza-tion problem for label consist K-singular value decomposition(LC-KSVD)have been carried out by using statis-tical modeling in dividing the HRRP's angular domain.Firstly,the maximum probability difference algorithm based on probabilistic principal component analysis is proposed to adapt HRRP angular domain to obtain the frame boundary.Secondly,based on LC-KSVD,the discriminant criterion is constructed by using the pow er spectrum of the frame boundary and the introduction of atomic sparse similarity error constraint in optimal dic-tionary selection to clarify test samples.The experimental results of the radar data show that this algorithm can improve the target recognition rate,and has good robustness to the noise interference.