Frequency diversity array (FDA) has been widely studied due to its nature of range-angle-dependent. However, the basic FDA generates an S-shaped range-angle coupled beampattern, which limits its application in target detection and localization. Recently, the FDA with sinusoidal frequency offset (Sin-FDA) has been proposed, which provides a narrow pencil beampattern, but at the cost of high sidelobes. In this letter, the FDA with modified sinusoidal frequency offset (MSin-FDA) is proposed. In MSin-FDA, The frequency offset of Msin-FDA can be flexibly adjusted by introducing extra configuration parameters. In addition, we use genetic algorithm (GA) to optimize the configuration parameters so as to obtain an enhanced beampattern. Simulation results demonstrate that, compared with the existing FDA schemes with nonuniform frequency offset, the proposed method has better spatial focusing, sidelobe suppression and array resolution performance under the same total frequency offset.

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.

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.

The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing $6K$ measurements and calculating $2K$ windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI.

Camera calibration is a core task in computer vision. Traditional calibration methods usually achieve approximate solutions due to the complexity of solving nonlinear equations, resulting in reprojection errors. This article proposes a camera calibration optimization algorithm based on the Starling-Inspired Strategy optimization algorithm, which improves calibration accuracy and stability by combining chaotic mapping and sine cosine optimization strategies. First, we constructed a real camera calibration image dataset that includes various calibration scenarios and calculated the initial values of camera calibration parameters based on Zhang’s calibration method. Then, we established a reprojection error model to evaluate the accuracy of the calibration parameters. Finally, we reduced the reprojection error through a hybrid optimization method based on the Steller Jay optimization algorithm. Experimental results show that our algorithm significantly reduces the reprojection error and improves the camera calibration accuracy.

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.