For many object tracking systems, how to quickly and efficiently estimate the direction of arrival (DOA) of radio waves impinging on the antenna array is an urgent task. In this paper, a new efficient DOA estimation approach based on the deep neural networks (DNN) is proposed, in which a nonlinear mapping that relates the outputs of the receiving antennas with its associated DOAs is learned by using the DNN-based network. The novel network architecture is divided into two phases, the detection phase and the DOA estimation phase. Additional detection network dramatically reduces the size of the training set and the process of the training data preparation is discussed in detail. After finishing the training phase, the corresponding DOAs can be identified based on current input data during testing phase. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also reduce the computational complexity required by traditional superresolution DOA estimation methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariance (ESPRIT). The computer simulation results are performed to investigate the generalization and effectiveness of the proposed approach in different scenarios.INDEX TERMS Deep neural networks (DNN), detection network, direction of arrival (DOA) estimation network, testing process, training data preparation process.

Although the platelet structure of calcium hexaluminate (CaAl12O19, or CA6) grains can strengthen and toughen the Al2O3–MgO–CaO system materials designed in the high‐alumina region, it also results in poor densification and subsequent accelerated slag penetration for refractory application. Considering this aspect, MgAl2O4–CaAl4O7–CaAl12O19 composite was fabricated by solid‐state reaction sintering in this work, and the effect of ZrO2 addition on densification and mechanical properties was investigated. The results showed that the CA6 grains presented a more equiaxed morphology by addition of ZrO2, contributing to form highly dense microstructures after heating at 1600°C without evident grain coarsening. The compressive strength and flexural strength were greatly enhanced mainly due to the significant decrease in porosity and pore sizes. Besides, the increased content of ZrO2 plays an active role in toughening this composite attributed to the dense microstructure and strong bonding with higher strength, as well as considerable t‐ZrO2 transformability.

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Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.

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