In this paper, a folded low-profile wideband 1-bit reconfigurable transmitarray (RTA) with 2D beam-scanning capacity is proposed. To reduce the profile of the whole RTA, the ray tracking mechanism and the feed optimization is introduced. A 1-bit wideband RTA element with polarization selection function and polarization rotation function is designed based on a regular hexagonal resonant patch and PIN diodes. Furthermore, a specially designed waveguide structure is fabricated to verify the performance of the RTA element. To achieve the low-profile design, a wideband polarization rotation reflectarray (PRRA) element acting as the reflecting surface is proposed and a 2×2 U-slotted planar patch antenna is designed to replace the horn. With these designs, the profile can be compressed by two-thirds compared with the conventional space-fed architecture. A 16×16-elements low-profile RTA is designed and fabricated. The measured and simulated radiation patterns are in good agreement. The measured peak gain is 19.6 dB, corresponding to an aperture efficiency of 13.8%, and the measured 3-dB gain bandwidth is 17.5% (from 15.1 GHz to 18 GHz). Good scanning performance with ±60° scanning coverage in both principal planes has been obtained, and the scan losses are 3.97 dB and 4.15 dB respectively. Besides, the measured SLLs and cross-polarization levels remain better than -15 dB and -20 dB, respectively, at all beam directions.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching. In this paper, we propose a new, simple yet effective neural network, termed SpinNet, to extract local surface descriptors which are rotation-invariant whilst sufficiently distinctive and general. A Spatial Point Transformer is first introduced to embed the input local surface into an elaborate cylindrical representation (SO(2) rotation-equivariant), further enabling end-to-end optimization of the entire framework. A Neural Feature Extractor, composed of point-based and 3D cylindrical convolutional layers, is then presented to learn representative and general geometric patterns. An invariant layer is finally used to generate rotation-invariant feature descriptors. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

Spectrum prediction is a promising technique of improving the spectrum exploitation in cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the energy consumption of spectrum sensing and improving the network throughput, etc. Recently, a significant amount of research efforts has been devoted to this area, especially deep learning (DL) methods, and greatly advanced spectrum prediction abilities. This paper reviews the state-of-the-art developments in DL for spectrum prediction. Specifically, we first summarize the existing spectrum prediction methods and give a taxonomy from different aspects. Second, we formulate long-term, multi-dimensional, and non-ideal spectrum prediction problems to provide theoretical support for spectrum prediction models. Third, we divide the DL models into four categories: basic models, models for the long-term domain, models for the multi-dimensional domain, and models for the non-ideal domain base on application scenarios and offer an in-depth tutorial on these models. Fourth, we comprehensively collect and organize widely used common spectrum data sources in the existing literature and also overview the spectrum data simulation methods to facilitate other researchers. Furthermore, we summarize and analyze the selection of evaluation metrics for spectrum prediction models based on DL. Finally, we summarize the research trends and highlight the critical research challenges.

In this paper, a 2-bit wideband electronically-controlled reconfigurable phased array with wide-angle beam-scanning capacity is proposed. Based on the current reversal mechanism and real-time delay design, a wideband 2-bit phase shifter is designed by cascading a wideband 180° phase shifter and a wideband 90° phase shifter. Then, a wideband 2-bit element is established by connecting the radiation area of the Vivaldi structure at the slot line of the phase shifter, and a metasurface slab with H-shaped metal layer is introduced to improve the scanning performance. Further, a planar wideband reconfigurable 2-bit array at 17 GHz has been designed and fabricated based on this element, and a stripline-based 1:16 power divider with the phase-shifting function is designed to simulate the space-fed spherical wave to avoid high sidelobe. Measured and simulated results are in good agreement. The measured results indicate that the main beam of the array can scan from -73° to 73° at 17 GHz by appropriately controlling the state of PIN diodes, and the gain fluctuation is only 2.18 dB. The measured 3-dB gain bandwidth of the array in the broadside direction is about 29.1% (from 14.7 GHz to 19.7 GHz). The wide-angle scanning and wideband performances are verified.

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.