Synthetic Aperture Radar (SAR) images are crucial for maritime vessel detection; however, challenges such as blurred ship edges, strong land scattering interference, and angular regression mismatches across varying target sizes hinder accurate rotational localization. In this paper, an oriented decoupling target detection method (R-MCLST) is proposed to address these issues. The method integrates three key modules: a multi-channel positioning module (MC-PM) that employs distributed average pooling and additional coordinate channels to enhance orientation awareness; a soft threshold-based multilayer perceptron (ST-MLP) that effectively mitigates background interference while robustly extracting complex features; and a Gaussian distribution-based prediction box (GD-BPB) that transforms rotated bounding box encoding into a two-dimensional Gaussian distribution using KL divergence for adaptive parameter adjustment. Experimental evaluations on the R-SSDD and MR-HRSID datasets demonstrate that R-MCLST achieves superior performance, with the R-SSDD dataset yielding AP50 of 87.48%, AP75 of 34.96%, AP of 41.15%, and AR of 46.52%, and the MR-HRSID dataset yielding AP50 of 61.59%, AP75 of 4.96%, AP of 19.13%, and AR of 22.24%. Comparative analyses confirm that the proposed method outperforms current state-of-the-art networks in accurately localizing rotating targets under challenging SAR imaging conditions.

In this article, a lightweight, flexible, wearable circularly polarized antenna for navigation and positioning in Internet of Things (IoT) applications is proposed. The proposed antenna is etched on a polyimide (PI) substrate with dimensions of 77.9 mm$\times 73$.2 mm$\times 0$.2 mm. The antenna consists of a feed branch, coupling branch, and peripheral annular ground, and it is fed using a coplanar waveguide (CPW) method. The fabricated antenna prototype demonstrates an operational bandwidth of 1100–2300 MHz, an axial ratio (AR) bandwidth of 1100–1700 MHz, and a half-power beamwidth (HPBW) of 101°. Measurement results align well with simulations, showing stable performance under various bending conditions and when worn at different body locations. Additionally, in outdoor satellite tracking tests, the proposed antenna was worn at various body positions, tracking up to 51 satellites with a positioning dilution of precision (PDOP) of 0.876. Compared with three commercial GNSS antennas, the proposed antenna offers a low profile and robust satellite-tracking performance, highlighting its potential as a navigation and positioning tool in IoT applications.

Ship detection in remote sensing images has been attracting a lot of attention due to its great application value in both military and civilian fields. However, ships in high-resolution remote sensing images are characterized by the remarkable features of multiscale, arbitrary orientation, and dense arrangement, which is a great challenge for fast and accurate target detection. In order to solve problems, we propose a YOLOV5-based oriented ship detection method of remote sensing images with contextual global attention mechanism and lightweight task-specific context decoupling (CGTC-RYOLO) in this article. First, a cross-stage partial context transformer (CSP-COT) module is introduced to capture global contextual spatial relations using multihead self-attention (MHSA) to verify their implications in implicit dependencies. Second, we propose an angle classification prediction branch in the YOLOV5 head network for detecting targets in any direction and design probability and distribution loss function (PrfoIoU) to optimize the regression effect. Third, the lightweight task-specific context decoupling (LTSCODE) for target detection is employed to replace the original head in the YOLOV5 model, which is used to solve the accuracy problem caused by YOLOV5’s hybridization of classification and localization. Ablation experiments demonstrate the importance and effectiveness of each module. Compared with the benchmark model, the CGTC-RYOLO has the 5.9%, 3.7%, and 4.3% mAP improvements on the DOTA-ship dataset, the HRSC2016 dataset, and the UCAS-AOD dataset, respectively. Moreover, the model’s generalization is also validated. Compared with state-of-the-artmethods, the CGTC-RYOLO can achieve better accuracy and fewer parameters.

Synthetic aperture radar (SAR) and optical images possess complementary strengths, offering rich spatial and spectral information. The intelligent classification of features through image fusion of SAR and optical presents both opportunities and challenges. However, fusion and intelligent classification encounter hurdles. Different physical properties and imaging principles between SAR and optical images often lead to sensor property mismatches, causing information loss. Moreover, optical images are susceptible to weather conditions, while SAR images suffer from scattering noise interference. In addition, the nonuniform distribution of feature categories results in sample imbalance. To address these problems, this article proposed a new fusion network structure dual-encoder net (DEN). First, without increasing the model complexity, considering the differences in the performance of features under different sensors, this network was keyed to a composition of two encoders that were able to utilize their respective features to encode and reduce the impact of modal differences. Second, a detail attention module (DAM) was constructed to capture the detailed information that was obscured by the optical image and acquired by the SAR image. Finally, a new loss function, comprising weighted information loss, pixel loss, and noise loss, was introduced to mitigate sample imbalance, retain key information, and reduce noise effects. The experimental results showed that the proposed method outperforms the current popular image fusion methods, and the model complexity was improved by 15.3% while the overall accuracy (OA) was improved by 2.6%, and the entropy, peak signal-to-noise ratio (PSNR), and mean square error (MSE) were improved by 29%, 27%, and 7%, respectively.

Ship classification in synthetic aperture radar (SAR) images is a challenge in the field of ocean monitoring. On the one hand, there are few labeled samples in SAR remote sensing ship datasets, and a commonly used single classification criterion cannot effectively represent the distribution of categories. On the other hand, the small size of the SAR ship and the inconspicuous appearance characteristics lead to the fact that the SAR ship samples are with less discriminative information; therefore, the rich feature space of a ship cannot be effectively obtained, which increases the difficulty of target distinguishability. A multibranch embedding network with bi-classifier (MBEN-BC) model was proposed to address these problems and for few-shot SAR ship classification. First, the MBEN module was utilized to extract the multiscale feature map spatial information of the input image at multiple levels and establish cross-channel information interaction so as to obtain discriminative features at the local and global levels, which effectively enriched the feature space. Then, the BC module was constructed to represent the image features from the image level and descriptor level, respectively, and the two classification criteria were presented to promote a more compact distribution of similar samples in the feature space in order to effectively represent the distribution of categories with a small number of labeled samples. Experimental validation was carried out using the FUSAR-Ship, Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and OPENSAR-Ship dataset, and the MBEN-BC method achieved superior performance and good generalization ability compared to the current popular and state-of-the-art few-shot methods.

BeiDou-3 navigation satellite system was officially opened in 2020. While bringing high-performance services to people around the world, the navigation system requires well-designed BeiDou antennas. In this paper, we propose a wideband circularly polarized high-performance BeiDou antenna. The antenna realizes wideband circularly polarized radiation through a four-port sequential feed network, and the phase imbalance of the feed network from 1.05 to 1.80 GHz is less than 7°. The manufactured antenna demonstrates a return loss of more than 13 dB and an axial ratio <3 dB over the entire global navigation satellite system (GNSS) frequency band. The right-handed circular polarization (RHCP) gain of the proposed antenna is greater than 4 dB in the GNSS low-frequency band and can reach more than 7.1 dB in the high-frequency band. Dimension of the proposed antenna is 120 mm×120 mm×20 mm, i.e., 0.54 λ o ×0.54 λ o ×0.09 λ o , where λ o is the wavelength of the center frequency. The proposed antenna connected to a GNSS receiver has tracked 12 BeiDou satellites with C/N 0 ratios of GNSS signals greater than 30 dB. Such a high-performance antenna provides a basis for high-quality positioning services.

In this paper, a dual-polarization dual-beam scanning array antenna based on holographic control theory is proposed. The antenna element integrates four PIN diodes, two in each of the horizonal and vertical directions, to achieve reconfigurable polarization. By varying the states of two PIN diodes in the same direction to change the radiation phase of the element, the simulation and measurement of the 2-element subarray verify that the proposed element is capable of manipulating the radiation pattern. Based on digital coding theory, the phase-adjustable structure integrated with the PIN diodes can act as a holographic element, and the desired object wave can be accurately and stably obtained by modulating the reference wave excited by the feed and the holographic element. The simulation verifies that the antenna array can achieve satisfactory 2D beam scanning. To verify the simulation results, a 2-element subarray prototype and a 6×12 array prototype are fabricated and subjected to measurement. The results of measurement and simulation are in good agreement, which proves the feasibility of the dual-polarization dual-beam scanning antenna system. Due to its low profile, low cost, and easy integration, this antenna system excels in applications in fields such as radar systems and smart antennas.

A wearable radio-frequency (RF) energy-localized harvester with multiple antennas and spoof surface plasmon (SSP) structure for multibeam radiation is presented to power the Bluetooth sensor module. The harvester contains four separate antennas connected by SSP waveguides, while the loop is applied to excite the structure to generate multibeam radiation and improve energy harvesting. The radiative waves will be harvested and converted into surface waves on the SSP waveguides, then, confined in a localized area of the structure through the evanescent field interactions between the current of SSP and the loop in a contactless way. More importantly, it is free of RF connectors and soldering works on the interface between the energy harvester and clothing. In addition, the wearable energy harvester is entirely made of flexible materials, such as conductive fabrics, polyimide, and nylon fabrics in a compact and low-profile structure. The loop can easily be integrated with the rectify circuit and power management unit (PMU) to provide direct-current (dc) power for different kinds of wireless sensor modules. In this design, the harvested power can support a battery-free Bluetooth temperature and humidity sensor with a power density of 2.75$\mu \text{W}$/cm2. The complete system-level demonstration of RF energy harvesting shows the potential to power small electronic devices for wearable applications.

Beam scanning antenna brings new opportunities to reduce the data collision of multinode communication in the Internet of Things (IoT). This article proposes a new type antenna design scheme that can be used for IoT relay communication. A programmable beam scanning antenna without phase shifters operating in the X-band is designed to evaluate the feasibility of this scheme. The integrated design of excitation source and phase-modulated structure greatly reduces the profile of the antenna and improves the integrated degrees of freedom with other equipment. By switching the state of the PIN diode loaded on each element, different radiators can be selected and the direction of current polarization on elements can be adjusted to change the radiation phase. Furthermore, the diode states are encoded by the holographic antenna theory and controlled by the FPGA controller to realize programmable beam scanning. One-element, two-element, and 1-D array containing 32 elements are fabricated and measured. The experimental results are in good agreement with the simulation data. The operating bandwidth is 7.5% at 10 GHz while the beam scanning range of the array is$- 60{^\circ }$–60°, with a good scanning accuracy and stability. The proposed low-profile beam scanning antenna can provide stable communication services to multiple users and smart devices as a new type of beam scanning array antenna solution for the IoT application, which is beneficial to this field.

A metasurface unit is designed operating at 2–20 GHz to enhance the gain and radiation performance of an antipodal Vivaldi antenna (AVA). The unit has a simple structure, stable ultra-wideband performance, high permittivity, and can independently modulate two polarization modes electromagnetic waves. We analyze the current distribution on the unit and extract equivalent characteristic parameters to verify the ability of independent modulation on two polarization modes electromagnetic waves. The designed metasurface unit is integrated into the aperture of the AVA and forms the metasurface lens (ML) for guiding the propagation of electromagnetic waves. Two types of ML are proposed and integrated into the AVA to design antennas Ant1 and Ant2. The modulation effect of the lens on the electromagnetic wave is analyzed from the perspective of electric field amplitude and phase, and the final design is obtained. From the optimized design results, the AVA and the proposed Ant2 are fabricated and measured, and the measurement results are in good agreement with the simulation ones. The impedance bandwidth measured by Ant2 basically covers the 2–18 GHz frequency band. Compared with the conventional AVA, the gain of the proposed Ant2 is increased by 0.6–3.7 dB, the sidelobe level is significantly reduced, and the directivity has also been clearly improved.

A novel estimator for the shape parameter of the generalized Gaussian distribution (GGD) is proposed based on the method of logarithmic cumulants. First, the expression of the log-cumulant for the GGD is theoretically derived. As a result, a simple equation for the estimation of the shape parameter is obtained. The processing procedure of the new estimator for practical applications is also provided. Numerical experiments are used to verify the excellent estimation performances of the proposed estimator for both large and small samples. Moreover, experiments using a measured CARABAS-II data set also validate the superiority of the proposed estimator.

Compact polarimetric data exploitation, especially in hybrid-polarimetric (HP) mode, is currently attracting increasing interest due to the new generation of synthetic aperture radar (SAR) systems. Recently, it has been demonstrated that the notch filter is useful for ship detection in either full- or dual-polarization (DP)-mode SAR images. In this paper, the notch filter investigation is further extended to HP SAR architecture for ship detection on the ocean surface. First, a version of the notch filter that is suitable for HP SAR is proposed based on the definition of the corresponding feature partial scattering vector from the covariance matrix of the HP SAR. Subsequently, a novel model characterizing the statistics of the notch distance of sea clutter in the HP mode is developed. Based on the statistical model, the threshold of constant false-alarm rate (CFAR) ship detection is theoretically and analytically derived, which allows the automatic and adaptive implementation for ship detection in varying sea backgrounds in practical applications. Experiments on the HP SAR data emulated from full-polarization L-band Aerospace Exploration Agency Advanced Land Observation Satellite Phased-Array type L-band SAR and C-band RADARSAT-2 SAR measurements validate not only the soundness of the proposed CFAR detection but also the high accuracy of the presented model in fitting HP SAR data. Furthermore, the notch filter and its CFAR realization provide the same benchmark for the comparison of the detectability of HP and conventional linear DP SAR data. Preliminary findings suggest that the detection performance of HP SAR is superior to that of DP SAR in ship observation. Therefore, the proposed CFAR method based on the notch filter provides a promising technique for the detection of ships using HP SAR data.

Based on its advantages, compact polarimetric (CP) synthetic aperture radar (SAR) is considered to be a good option for earth observations. This paper proposes an adaptive method of ship detection in CP SAR images operating in the hybrid-polarimetric (HP) mode. First, according to the analysis of scattering between ships and sea background, a novel decomposition approach, named power-entropy (PE) decomposition, is developed. Based on this approach, two components of the scattering power, the high-entropy scattering amplitude (HESA) and low-entropy scattering amplitude, are separated. We demonstrate that the HESA component is an effective physical quantity indicating the difference between the ship and its background and hence can potentially be used for ship detection using HP SAR data. The generalized Gamma distribution (GΓD) is found suitable for the characterization of HESA statistics of sea clutter with a wide range of homogeneity. As a result, the adaptive constant false-alarm rate (CFAR) technique based on the HESA detector is proposed. Experiments performed using HP measurements emulated from L-band ALOS-PALSAR and C-band RADARSAT-2 full polarimetric data validate the soundness and superiority of the proposed CFAR method based on the HESA detector. Both the theoretical proof and experimental results show that the HESA improves the ship-sea contrast (or the signal-clutter ratio) more than popular detectors, such as the entropy and SPAN. Moreover, the GΓD is a versatile model for the description of the statistical behavior of both the HESA and comparable detectors.

Accurate knowledge of the statistical properties of synthetic aperture radar (SAR) amplitude data is essential for the processing and interpretation of SAR images. During the last few decades, parametric models have been extensively investigated for use in characterizing the statistics of SAR amplitude data. Among them, the generalized gamma distribution ($\text{G}\Gamma \text{D}$) has been widely applied in many fields of SAR image processing, as it has been demonstrated to be appropriate for describing the statistical behaviors of both SAR land and sea clutter. However, the$\text{G}\Gamma \text{D}$is not effective when the third-order sample log-cumulant of SAR data is close to zero as it cannot match the distribution of actual data. In this case, the recently proposed${\mathcal{ G}}_{\textrm {AO}} $model shows good performance for fitting the distribution of the actual SAR data. In this paper, we theoretically derive the analytical conditions of the${\mathcal{ G}}_{\textrm {AO}} $model applicability. Based on the derivations, a scheme describing the statistical behaviors of SAR pixel amplitudes is given by combining the$\text{G}\Gamma \text{D}$and the${\mathcal{ G}}_{\textrm {AO}} $model. Experiments performed on C-band RADARSAT-2 and L-band ALOS-PALSAR SAR data verify the effectiveness of the proposed scheme. The usefulness of the${\mathcal{ G}}_{\textrm {AO}} $model for extremely heterogeneous land clutter, for example, from urban regions, is also demonstrated.