In this letter, we propose a deep learning-based off-grid Direction of Arrival (DOA) estimation method for low Signal-to-Noise Ratio (SNR) scenarios. Specifically, we develop a dual-branch neural network with residual connections that processes frequency-domain features, consisting of a coarse classification branch and a fine regression branch. The classification branch employs a multi-label approach to obtain on-grid results, while the regression branch predicts the residual between the classification outputs and ground-truth angles. This structural design effectively leverages classification results to avoid convergence difficulties associated with direct off grid angle prediction, thereby enhancing DOA estimation accuracy. Simulation results demonstrate that under low SNR conditions, the proposed method outperforms existing approaches, including both classical model-based and other deep learning-based methods.

Abstract The height measurement algorithm of metre wave multiple input multiple output (MIMO) radar based on uniform linear array (ULA) has some problems, such as a sharp decline of angle measurement accuracy of ultra‐low altitude targets and large fluctuation of height measurement with the change of elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, coprime array (CPA) is applied to the height measurement of monostatic metre wave MIMO radar. In this paper, the signal model of monostatic CPA metre wave MIMO radar is established under the condition of flat ground, a height measurement method using physical array is proposed based on the derivation that it is infeasible to use virtual array method for height measurement, it fills the gap of low elevation estimation method based on sparse array metre wave MIMO radar. The proposed method calculates the echo data according to the characteristics of the steering vector of CPA, then obtains the accurate low elevation by using the generalised multiple signal classification algorithm, and finally the target height is obtained by using the geometric relationship. Simulation experiments compare three typical CPAs with ULA using equal array elements, and verify the superiority of height measurement performance of monostatic metre wave MIMO radar based on CPA and the effectiveness of the proposed method.

Abstract The tensor‐based spatial smoothing is a good algorithm for the direction of arrival (DOA) estimation, but it cannot make good use of the physical aperture of the array, so the paper proposes a tensor‐based array virtual translation DOA estimation algorithm to solve the problem. Under the framework of tensor‐based DOA estimation algorithm, the factor matrix obtained by tensor decomposition is extended to a signal subspace with Vandermonde structure by using array virtual translation technology. In addition, the algorithm expands the available array aperture, breaks through the limitation of physical array aperture on multi‐target estimation ability, and further improves the estimation accuracy. Since the processing technique proposed in this paper has nothing to do with the construction of tensors, this technique is suitable for conventional DOA estimation algorithms based on tensors. Theoretical analysis and simulation experiments verify the effectiveness of the algorithm proposed in this paper.

Wideband interference (WBI) is detrimental to high-resolution radar due to its high power and wide frequency occupancy. In this study, a deep learning (DL) method is proposed to predict the time–frequency (TF) feature of WBI and applied to cognitive radar high-resolution range profile (HRRP) estimation. Specifically, by performing short-time Fourier transform (STFT) on the WBI signal collected in the past and using a sliding window, a series of WBI TF figures is generated. A long short-time memory (LSTM) network is then used to learn the spatiotemporal (ST) correlation of these TF figures, thus predicting the WBI TF feature in the future, based on which, a cognitive method is used for target HRRP estimation with reduced influences of WBI. Numerical results demonstrate the effectiveness of the proposed methods.

The mutual coupling among various components of the collocated crossdipole (CCD) vector‐sensor is severe, and its application is greatly limited. The spatial spread dipole (SSD) vector‐sensor can avoid this problem, but the multiple signal classification (MUSIC) algorithm for the SSD array is rarely developed. In view of this situation, this paper proposed a MUSIC‐like algorithm for the SSD array. The biquaternion model was first established, and the biquaternion MUSIC (BQ‐MUSIC) algorithm was developed on the basis of this model, for the two‐dimensional direction‐of‐arrival (2D‐DOA) estimation. Our proposed algorithm requires low computational complexity by adopting the dimensionality reduction method. Numerical simulations verify the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR) system, the separation and reconstruction of useful signal from Narrow-band interference (NBI) and Wide-band interference (WBI) components is a challenging problem. In this paper, a novel time-varying interference suppression algorithm is proposed based on the signal synthesis from joint time-frequency (TF) distribution. This algorithm makes full use of two TF representations: Wigner distribution (WD) and cross WD (CWD). After cross-terms elimination, these two TF representations are equal or close to the sum of WDs or CWDs of individual signal components, respectively. Based on this property, interferences can be separated and reconstructed by matrix rearrangement and eigenvalue decomposition (EVD). Compared with the traditional SSM (TSSM), the proposed algorithm has two advantages: 1) it is more accurate, since it avoids the approximate interpolation to WD; 2) it is quite time-saving, due to its matrix obtained by fast Fourier transform (FFT) and matrix rearrangement instead of the discrete Fourier transform (DFT). Experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and computational complexity.

The contest against electromagnetic spectrum are making the electromagnetic environment more and more congested. Synthetic aperture radar (SAR) requires larger bandwidth to obtain finer resolution, and thus inevitably affected by radio emitters sharing the same frequency band. Most of the radio frequency interference (RFI) originated from the terrestrial emitters, while there are also rare cases with interfering signals from space-borne satellites. In this paper, we analyzed the mechanism of terrain scattered interference (TSI) from space-borne RFI sources, and provide a case study of the interference signatures in Sentinel-1 data.

For polarimetric multi‐input multi‐output (MIMO) radar with spatially spread crossed‐dipole, this article studies the problem of joint direction of arrival (DOA) and polarisation parameter estimation based on block‐orthogonal matching pursuit (BOMP) algorithm. First, the signal model of polarimetric MIMO radar with spatially spread crossed‐dipole is established, and then the covariance matrix of the received data is calculated. Using the relationship between polarisation parameter and DOA in the crossed‐dipole, sparse dictionary matrix is constructed within only DOA parameter and it will be translated into a block sparse problem. Then, fast BOMP algorithm is used to estimate their support positions and their amplitudes. Last, DOA estimation is calculated by support positions and polarisation parameter is estimated by the amplitudes of the support positions. The proposed algorithm has three advantages. One is that overcomplete dictionary is constructed within only the DOA, which has a small computational complexity. Another one is that the problem of strong mutual coupling among collocated crossed‐dipole is solved by using the spatially spread crossed‐dipole. The last one is that the DOA and polarisation estimations can pair automatically without any additional processing. Computer simulation results demonstrate the effectiveness of the proposed algorithm.

This study proposes a new polarisation smoothing algorithm with multiple‐input and multiple‐output (MIMO) electromagnetic vector sensor array to address the problem of coherent source angle estimation in MIMO radar. This algorithm can be summarised as follows: (i) matched filtering for echoes is performed to get virtual MIMO array; (ii) the virtual array is divided into six spatially identical subarrays according to polarisation information offered by electromagnetic vector sensor; and (iii) the six subarrays covariance matrices are processed with weighted smoothing to obtain polarisation smoothing covariance matrix, which can restore the rank loss of the covariance matrix of coherent sources. When compared with spatial smoothing, the proposed algorithm is suitable for spatially arbitrary array configuration, without the price of reducing effective array aperture. Simulation results show that the proposed algorithm substantially outperforms the spatial smoothing algorithm.