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This letter proposes an efficient tensor model for Direction of Departure (DOD) and Direction of Arrival (DOA) estimation based on bistatic nested Multiple‐Input Multiple‐Output (MIMO) radar. Tensor offers a robust solution in characterizing multi‐dimensional data structures, ensuring data integrity, and has demonstrated remarkable effectiveness in the realm of multi‐dimensional parameter estimation. However, the complexity of traditional methods for constructing tensors is a major obstacle to practical applications. In response, this research introduces a novel approach that utilizes an iterative two‐dimensional Toeplitz matrix reconstruction method to rapidly derive expanded virtual covariance matrices prior to tensor construction. This streamlines the algorithm and improves efficiency. Numerical simulations have verified the superiority of this algorithm. 1. The proposed approach utilizes Toeplitz matrix‐based quadratic reconstruction to address rank deficiency issues and obtain an equivalent virtual covariance matrix with minimal complexity. 2. Results demonstrate that this approach achieves reduced computational complexity while maintaining high estimation performance.

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.

For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network). The algorithm firstly inputs the signal covariance matrix of the array of the low-elevation target containing direct and reflected waves into the convolutional autoencoder to realize the de-multipath, and uses the spatial features extracted by the convolutional autoencoder as the input of the extreme learning machine to realize the DOA preclassification of direct waves; based on the preclassification results, one branch of the three parallel convolutional neural nets is selected, and the output of the convolutional autoencoder is used as the input of this branch to realize DOA estimation. The simulation results show that the algorithm has better estimation accuracy and efficiency than the conventional algorithms, especially when the DOA of the target is in the lower range. The analysis of the simulation results shows that the algorithm is effective, in which the convolutional autoencoder can effectively realize the de-multipath, and the use of parallel convolutional neural networks can avoid overfitting and underfitting and realize DOA estimation more accurately.

Sparse arrays are of deep concern due to their ability to identify more sources than the number of sensors, among which the hole-free difference co-array (DCA) with large degrees of freedom (DOFs) is a topic worth discussing. In this paper, we propose a novel hole-free nested array with three sub-uniform line arrays (NA-TS). The one-dimensional (1D) and two-dimensional (2D) representations demonstrate the detailed configuration of NA-TS, which indicates that both nested array (NA) and improved nested array (INA) are special cases of NA-TS. We subsequently derive the closed-form expressions for the optimal configuration and the available number of DOFs, concluding that the DOFs of NA-TS is a function of the number of sensors and the number of the third sub-ULA. The NA-TS possesses more DOFs than several previously proposed hole-free nested arrays. Finally, the superior direction of arrival (DOA) estimation performance based on the NA-TS is supported by numerical examples.

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.

Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generalized nested array (GNA), and the difference of coprime factors is employed as the spacing between the second subarray and the additional antenna. Then, the values of the coprime factors are analyzed in detail, which indicates that Yang-NA can be explained as a special case. Compared with the majority of the existing sparse arrays, EGNA not only has the closed-form expressions of the physical antenna locations, consecutive lags, and unique lags, but also significantly increases DOF and reduces MC. In view of the above advantages, EGNA can obtain superior performance in direction of arrival (DOA) estimation. Numerical simulation results verify the rationality and superiority of the proposed nested array.

Aiming at the problem of the small aperture of the traditional MIMO radar with virtual degrees of freedom, this paper designs a high degree of freedom space-limited MIMO radar. Both the transmitting and receiving elements of this radar adopt a sparse array structure. Array composition, the receiving array element is composed of a single array element and a uniform linear array. The number of virtual array elements can be realized by using array elements. Compared with the traditional sparse array MIMO radar with the same number of elements, the designed space-limited sparse array MIMO radar has a larger aperture. Experimental simulations verify the superiority of the space-limited MIMO radar angle estimation.

In this paper, we initiated a method to estimate the direction of arrival (DOA) of far-field, narrowband, and incoherent targets using coprime array. First, we proposed a coprime array structure and analysed the distribution of difference coarray (DCA). The degrees of freedom (DOF) of the proposed coprime array became clearer by referring to the DCA conception. However, previous algorithm only uses the continuous virtual array, which causes the virtual array elements in the repeated position being abandoned. Therefore, the paper analyses the distribution of virtual array based on DCA conception and averages the receiving signal on these redundant virtual array elements to increase the utilization of receiving data. As a result, the algorithm has high precision in parameter estimation. Simulation results have shown the superiority of the proposed algorithm.

The redundant virtual array elements formed by the sum and difference coarray (SDCA) are always discarded in the existing DOA estimation methods, which could cause effective information loss and poor performance. In this paper, a new technology based on average processing of redundant virtual array elements is proposed for coprime MIMO radar direction of arrival (DOA) estimation. Then, Toeplitz matrix reconstruction based on MUSIC algorithm is employed to validate the effectiveness of the proposed technology for DOA estimation.

Background Pancreatic cancer, one of the top two most fatal cancers, is characterized by a desmoplastic reaction that creates a dense microenvironment, promoting hypoxia and inducing the epithelial-to-mesenchymal transition (EMT) to facilitate invasion and metastasis. Recent evidence indicates that the long noncoding RNA NORAD may be a potential oncogenic gene and that this lncRNA is significantly upregulated during hypoxia. However, the overall biological role and clinical significance of NORAD remains largely unknown. Methods NORAD expression was measured in 33 paired cancerous and noncancerous tissue samples by real-time PCR. The effects of NORAD on pancreatic cancer cells were studied by overexpression and knockdown in vitro. Insights into the mechanism of competitive endogenous RNAs (ceRNAs) were gained from bioinformatics analyses and luciferase assays. In vivo, metastatic potential was identified using an orthotopic model of PDAC and quantified using bioluminescent signals. Alterations in RhoA expression and EMT levels were identified and verified by immunohistochemistry and Western blotting. Results NORAD is highly expressed in pancreatic cancer tissues and upregulated in hypoxic conditions. NORAD upregulation is correlated with shorter overall survival in pancreatic cancer patients. Furthermore, NORAD overexpression promoted the migration and invasion of pancreatic carcinoma cells, while NORAD depletion inhibited EMT and metastasis in vitro and in vivo. In particular, NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p, thereby promoting EMT. Conclusions Elevated expression of NORAD in pancreatic cancer tissues is linked to poor prognosis and may confer a malignant phenotype upon tumor cells. NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p. This finding may contribute to a better understanding of the role played by lncRNAs in hypoxia-induced EMT and provide a potential novel diagnostic and therapeutic target for pancreatic cancer.