A low-profile broadband circularly polarized (CP) patch antenna with wide axial-ratio beamwidth (ARBW) is presented in this paper. The proposed antenna adopts two substrates, one of which is printed with a feeding network composed of the Wilkinson power dividers cascaded with the Schiffman phase shifters to contribute to the broadband CP property. A circular patch with four L-shaped branches as a radiator printed on another substrate is employed to obtain wide axial-ratio beamwidths (ARBWs). A wideband CP radiation is achieved by utilizing TM ₁₁ , TM ₁₁₊ and TM ₃₁ mode. An antenna prototype with dimensions of 1.03 λ ₀ × 1.03 λ ₀ × 0.025 λ ₀ (λ ₀ is the wavelength at the center frequency) is fabricated. The measurement results reveal that a wide relative impedance bandwidth of about 55.6% can be achieved in the 3.26–6.04 GHz, and the CP bandwidth is 33.0% (3.85–5.50 GHz). At 5.0 GHz, the 3 dB ARBWs of the xoz plane and the yoz plane are 148° and 220°, respectively.

A wideband millimeter-wave (mmW) circular aperture horn antenna with a conical radiation pattern realized by the metallic 3-D printing technology is presented in this paper. The proposed structure consists of a coaxial waveguide and a quadruple-ridged conical horn. The horn antenna was fabricated using Direct Metal Laser Sintering (DMLS) technology, and both manual polishing and yellow-gold coating were employed to realize a surface roughness of Ra = 1.6161 μm. The overall size of the fabricated antenna is 20 mm × 20 mm × 15.8 mm. Experimental measurements show that a wide relative impedance bandwidth of 60.7% and a peak gain of 11.4 dBi can be achieved within the frequency band of 24.9 GHz - 46.6 GHz.

For metal crack nondestructive detection, most conventional crack sensors are unable to realize the crack direction detection. In this work, a direction-sensitive microwave sensor is proposed for metal crack detection. The proposed sensor consists of a rectangular patch resonator and two perpendicular coupled feeding ports, which improves the current distribution on patch surface and the sensitivity for crack direction detection. The performances of the proposed sensor are verified by simulation and measurement experiments. The results show that the width sensitivities of two feeding ports are 100 MHz/mm and 63.3 MHz/mm, respectively, and the sensitivity of the sensor for crack direction detection are 6.10 MHz/5 degrees and 1.93 MHz/5 degrees, respectively. Due to the advantages of a simple structure, low profile, large coverage area and high sensitivity, the proposed sensor has a great application potential in nondestructive detection fields.

A 3-D printed antenna subsystem, which proves its effectiveness in cost- & weight-reduction and reliability improvement over conventional CNC fabrication, is proposed in this paper. The proposed structure which integrates an orthogonal-mode transducer (OMT) and a corrugated horn is fully compatible to fabrication using high temperature resistant resin Acrylonitrile Butadiene Styrene (ABS) with 3-D printing technology, as well as coating using copper with electroless metallization method. Detailed design procedures are provided and a prototype operating at $X$ -band is designed, fabricated and measured. Over the whole operating frequency range of 11.7 GHz~12.7 GHz, measured reflection coefficients are better than −22 dB and −12 dB for different polarization directions. A very good isolation better than 60 dB is achieved between two input ports. The cross polarization level is at least 30 dB lower than the co-polarization. Moreover, the effectiveness of the fabricated antenna is also verified in a polarimetric radiometer system.

This paper proposes an energy-efficient and high-accuracy gesture recognition framework to address the challenges of high computational complexity and interference susceptibility in conventional radar-based gesture recognition methods. Firstly, an enhanced Gaussian Mixture Model (GMM) with an optimized learning rate is introduced to improve anti-interference performance by exploiting spatial and velocity differences between gesture signals and target-like interference. Furthermore, a novel Spiking Neural Network (SNN) architecture is, proposed combining a 2D Convolutional Neural-Network (2D-CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for capturing, longterm temporal dependencies. This hybrid architecture effectively integrates short-term and long-term temporal dynamics to enhance recognition accuracy. Additionally, Spike-Timing-Dependent Plasticity (STDP) is incorporated to address the non-differentiability of spike-based data, thereby improving the network’s feature learning capabilities. To evaluate the proposed approach, a radar-based gesture dataset comprising seven gesture categories was constructed using a 60 GHz Frequency-Modulated Continuous Wave (FMCW) radar system. Experimental results demonstrate a recognition accuracy of 99.28%, alongside computational complexity and power consumption have better performance than the existing competitive methods, suiting power and resource-constrained environments.

Due to the range-angle two-dimensional beam control ability of frequency diverse array multiple-input multipleoutput (FDA-MIMO) radar, it has a wide range of application prospects in mainlobe interference suppression. Several robust beamforming methods for FDA-MIMO radar have been proposed in recent years. However, most of these methods require sufficient independent and identically distributed snapshots, which is difficult to satisfy for FDA-MIMO radar, especially in dynamic environments. Therefore, the problem of FDA-MIMO robust beamforming in sample-starved scenarios is considered in this paper, especially for an extreme case where only a single snapshot can be used for beamforming. This work proposes an improved single-snapshot robust beamforming method for FDA-MIMO radar. We eliminate the desired target component by forward-backward spatial smoothing, expand the number of samples, and construct a robust beamforming problem based on the worst-case performance optimal (WCPO) criterion. To reduce the computational complexity, the original problem can be divided into two convex optimization subproblems using the Kronecker product (KP) structure of the steering vector. The optimal weight vector can be obtained by iteratively solving the two convex optimization subproblems. In addition, the Shrinkage method is introduced to alleviate the mismatch of the covariance matrix caused by the lack of snapshots. Numerical examples show that the proposed method has better performance than the existing competitive algorithms in terms of output signal-to-interference-plus-noise ratio (SINR), robustness, and computational complexity.