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To address the problems of traditional two‐level nested arrays (TTNA) with low degrees of freedom and high mutual coupling, a generalised two‐level nested MIMO radar array with high degrees of freedom and low mutual coupling for the direction of arrival (DOA) estimation is proposed. First, two coprime expansion factors are introduced in the TTNA to mitigate mutual coupling among array elements and enhance its degrees of freedom. Secondly, the range of consecutive lags and the total number of virtual array elements are derived in closed form under the structures of 'sum–difference co‐array'. Finally, utilising the atomic norm minimisation theory, the authors address the issue of discrete holes caused by different coprime expansion factors through filling in virtual arrays at discontinuities. Furthermore, a convex optimisation model to recover equivalent received signals from these filled virtual arrays is established, which is combined with a multiple signal classification algorithm for DOA estimation. Simulation results validate the rationality of the array structure and demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which "1" indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input. [ABSTRACT FROM AUTHOR]