In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and $\ell_1$ -norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments without any time-consuming problem.

In this letter, a hybrid multiorder wide bandpass frequency selective surface (FSS) with high selectivity and low insertion loss is achieved by combining the two-dimensional (2-D) FSS structure and the 3-D vertical structure. The 3-D periodic vertical printed circuit board pieces (PVPCB) are used as the dielectric layer in the unit cell to achieve a proper band flatness without introducing great insertion loss. The multiorder response of the proposed FSS is achieved using a series of 3-D PVPCB instead of stacking the 2-D FSS layers. The proposed FSS unit cell is constructed by combining the full-wave and equivalent circuit simulations, since the physical structures have a good one-to-one correspondence with the circuit elements. The module design method is used to accelerate the design procedure, and the simulation result proves the effectiveness of this design method. Finally, the prototype is fabricated and measured to prove the effectiveness of the proposed FSS. The measured relative bandwidth and rectangle coefficient achieved is 130.4% and 0.76, respectively. The measured maximum insertion loss over the whole passband is 0.7 dB. Because of its strong capability of out-of-band rejection, the proposed FSS structure is a good candidate to enhance high-frequency electromagnetic pulse protection of wideband radar systems.

In this study, a novel compact printed monopole antenna for ultra-wideband systems is presented. The antenna exhibits an ultra-wideband frequency cover from 2.45 to 11.0GHz. The radiation pattern of the proposed antenna is nearly omni-directional in the H-plane over the entire operating bandwidth. Microwave experiments and numerical simulations are performed to demonstrate its functionalities. Good agreement is obtained between the simulated and the measured antenna characteristics.

Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around $$10\%$$10 % . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of $$3^\circ $$3 ∘ . Moreover, the proposed estimation network has root mean square estimation error lower than $$1^\circ $$1 ∘ when signal noise ratio equals $$-\,10\,{\mathrm {dB}}$$- 10 dB and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments.