Search Results (1,295)

Sea fog is a hazardous natural phenomenon that reduces visibility, posing a threat to ports and nearshore navigation, making the identification of nearshore sea fog crucial. Millimeter-wave radar has significant advantages over satellites in capturing sudden and localized sea fog weather. The use of millimeter-wave radar for sea fog identification is still in the exploratory stage in operational fields. Therefore, this paper proposes a nearshore sea fog identification algorithm that combines millimeter-wave radar with multiple machine learning methods. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to partition radar echoes, followed by the K-means clustering algorithm (KMEANS) to divide the partitions into recognition units. Then, Sea-Fog-Recognition-Convolutional Neural Network (SFRCNN) is used to classify whether the recognition units are sea fog areas, and finally, the partition coverage algorithm is employed to improve identification accuracy. The experiments conducted using millimeter-wave radar observation data from the Pingtan Meteorological Observation Base in Fujian, China, achieved an identification accuracy of 96.94%. The results indicate that the proposed algorithm performs well and expands the application prospects of such equipment in meteorological operations. Full article

Direct wireless communication through sea–air media is essential for constructing an integrated communication network that spans space, air, land, and sea. The amplitude of acoustically induced micromotion surface waves is much smaller than the noise interference in complex sea states, making the accurate extraction of these signals from the raw signals detected by an FMCW millimeter-wave radar a major challenge. In this paper, Butterworth filtering is used to extract underwater acoustic signals from the surface waves detected by radar. The physical processes of the channel were simulated theoretically and verified experimentally. The results demonstrate a fitting coefficient of 0.99 between the radar-detected water surface waves and the simulation outcomes, enabling the effective elimination of noise interference and the extraction of acoustically induced micromotion signals in environments with a signal-to-noise ratio (SNR) of −20 dB to −10 dB. Experiments modifying frequency and linear frequency modulation have verified that the usable frequency range for underwater acoustic signals is at least 400 Hz, meeting the frequency requirements of Binary Frequency Shift Keying (2FSK) modulation encoding methods. This research confirms the accuracy of the simulation results and the feasibility of filtering and extracting underwater acoustic signals, providing a theoretical basis and an experimental foundation for building cross-media communication links. Full article

This paper introduces a novel, small-sized, highly integrated, circularly polarized wide-angle scanning antenna using substrate-integrated waveguide (SIW) technology at millimeter-wave frequencies. The antenna unit addresses requirements for high data transmission rates, wide spatial coverage, and strong interference resistance in communication systems. By integrating radiating square waveguides, circular polarizers, filters, and matching loads, the antenna enhances out-of-band suppression, eliminates cross-polarization, and reduces manufacturing complexity and costs. Utilizing this antenna unit as a component, a 4 × 4 phased array antenna with a two-dimensional ±60° scanning capability is designed and simulated. The simulation and measurement results confirm that the phased array antenna achieves the desired scan range with a gain reduction of less than 3.9 dB. Full article

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. Full article

In this work, we resolve the cascaded channel estimation problem and the reflected channel estimation problem for the reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. The novel two-step method contains modified multiple population genetic algorithm (MMPGA), least squares (LS), residual network (ResNet), and multi-task regression model. In the first step, the proposed MMPGA-LS optimizes the crossover strategy and mutation strategy. Besides, the ResNet achieves cascaded channel estimation by learning the relationship between the cascaded channel obtained by the MMPGA-LS and the channel of the user (UE)-RIS-base station (BS). Then, the proposed multi-task-ResNet (MTRnet) is introduced for the reflected channel estimation. Relying on the output of ResNet, the MTRnet with multiple output layers estimates the coefficients of reflected channels and reconstructs the channel of UE-RIS and RIS-BS. Remarkably, the proposed MTRnet is capable of using a lower optimization model to estimate multiple reflected channels compared with the classical neural network with the single output layer. A series of experimental results validate the superiority of the proposed method in terms of a lower norm mean square error (NMSE). Besides, the proposed method also obtains a low NMSE in the RIS with the formulation of the uniform planar array. Full article

A Rectangular Dielectric Resonator Antenna (RDRA) design for mmWave-frequency-band MIMO metrics is proposed, with a compact, low-complexity, high-gain structure that is easy to fabricate and offers reduced inter–port isolation. The RDRA operates in the mmWave spectrum, featuring a compact size of 1.307${\lambda }_0$ × 1.307${\lambda }_0$, an impedance bandwidth of 6%, and a resonant frequency of 28 GHz, with a peak gain of 7 dBi. A four element MIMO system iteration was developed while maintaining the performance of the single element antenna. Additionally, a simple, low-complexity slot-etching technique was applied to achieve an average inter-port element isolation of 14 dB. The design also achieved a novel four-beam petal-splitting radiation pattern. The MIMO metrics, with an envelope correlation coefficient (ECC) of <0.5 and a diversity gain (DG) < 10, were successfully met. The simulated and measured results are in good agreement. Full article

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. Full article

Unmanned aerial vehicles (UAVs) need high data rate connectivity, which is achievable through mm-waves and sub-THz bands. The proposed two-port leaky wave MIMO antenna, employing a coplanar proximity technique that combines capacitive and inductive loading, addresses this need. Featuring mesh-like slots and a vertical slot to mitigate open-stopband (OSB) issues, the antenna radiates broadside and bidirectionally. H-shaped slots on a strip enhance port isolation, and a coffee bean metasurface (MTS) boosts radiation efficiency and gain. Simulations and experiments considering various realistic scenarios, each at varying vertical and horizontal distances, show steered beam patterns, circular polarization (CP), and high-gain properties, with a maximum gain of 13.8 dBi, an axial ratio (AR) <2.9, a diversity gain (DG) >9.98 dB, and an envelope correlation coefficient (ECC) <0.003. This design supports drones-to-ground (D2G), drone-to-drone (D2D), and drone-to-satellite (D2S) communications. Full article

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. Full article

A packaged transmit array (TA) antenna is designed for automotive radar applications operating at 77 GHz. The compact dimensions of the proposed configuration make it compatible with standard quad flat no-lead package (QFN) technology. The TA placed inside the package cover is used to focus the field radiated by a feed placed in the same package. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. An analytical approach is employed to evaluate the primary parameters of the suggested TA, including its directivity, gain and spillover efficiency. The final design has been refined using comprehensive full-wave simulations. The simulated gain is 14.2 dBi at 77 GHz, with a half-power beamwidth of 22°. This proposed setup is a strong contender for highly integrated mid-gain applications in the automotive sector. Full article

Millimeter-wave (mmWave) antenna arrays are pivotal components in modern wireless communication systems, offering high data rates and improved spectrum efficiency. However, the performance of mmWave antenna arrays can be significantly affected by structural distortions, such as mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. In this paper, we present a comprehensive sensitivity study of mmWave antenna arrays to structural distortion, employing a coupled structural–electromagnetic statistical concept. The proposed model integrates structural analysis techniques with electromagnetic simulations to assess the impact of structural distortions on the performance of mmWave antenna arrays. In addition, the model incorporates random element positioning, making it easy to analyze radiation pattern sensitivity to structural deformation. Demonstrating the applicability of the model, a 10 × 10 microstrip patch antenna array is designed to assess the performance of the model with a random position error and saddle shape distortion. The results of the model are then compared against the acceptable results from the HFSS software (version 13.0), where a good agreement is observed between the two results. The results show the gain variation and sidelobe level under various degrees of distortion and random errors, respectively. These results provide a guide for design, deployment, and optimization of mmWave communication networks in real-world environments. In addition, the model provides valuable insights into the trade-offs between antenna performance, structural integrity, and system reliability, paving the way for more efficient and dependable mmWave communication systems in the era of 5G and beyond. Full article

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input–multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML)-based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP), consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing the overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks. Full article

A wideband dual-beam dielectric resonator antenna (DRA) with substrate integration capability was proposed for millimeter-wave (mm-wave) applications. The four rows of air vias along the x-direction and two extended rectangular patches could shift the undesirable radiation mode upward and move the conical-beam radiation mode downward, respectively. Thus, the TE₂₁₁ mode and the TE₄₁₁ mode of the patch-loaded perforated rectangular substrate integrated dielectric resonator (SIDR) supporting the dual-beam radiation can be retained in the operating band, and their radiation can be improved by the air vias along the y-direction. The T-shaped line coupled dual-slot structure could excite the above two modes, and a dual-slot mode supporting dual-beam radiation could also work. Then, a wideband DRA with a stable dual-beam radiation angle can be achieved, and its impedance matching can be improved by two air slots on two sides. Compared with the state-of-the-art dual-beam antennas, the proposed antenna shows a wider bandwidth, a higher radiation efficiency, and the substrate integration capability of DRA, making it more suitable for mm-wave applications. For demonstration, a 1 × 4 array was designed with the 10 dB impedance matching bandwidth of 41.2% and the directions of the dual beams between ±30° and ±35°. Full article

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method. Full article

Enhancing road safety by monitoring a driver’s physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver’s seat. This article mainly describes how advanced signal processing methods, including the Huber–Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system’s effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments. Full article