<sec>To meet the growing demand for high-frequency broadband signal processing in complex electromagnetic environments and to overcome the limitations of traditional electronic systems such as restricted bandwidth, limited response speed, and low integration density, this paper presents a reconfigurable microwave photonic channelized receiver chip implemented on a silicon photonic platform. The proposed architecture adopts a two-stage optical filtering strategy that circumvents the typical strict wavelength alignment requirements in traditional designs, thereby greatly alleviating the challenges of system integration. In the first stage, the cascaded Mach-Zehnder interferometer (MZI)-based wavelength division multiplexers (WDMs) are used to perform Gaussian-shaped filtering of the input optical spectrum with a channel spacing of approximately 200 GHz. The second stage combines an array of coupled resonator optical waveguide (CROW) filters functioning as finely tunable bandpass elements. These CROW filters utilize curved waveguide directional couplers, which are specifically designed to address the issues found in traditional multimode interference (MMI) couplers such as high insertion loss—and in straight directional couplers, which encounter significant coupling dispersion. The optimized curved coupler exhibits an insertion loss below 0.03 dB and a coupling ratio variation of less than 10% across the 1500–1600 nm wavelength band. Filter bandwidth reconfigurability is achieved via thermo-optic tuning of the balanced MZI embedded within each CROW filter, enabling dynamic adjustment of the coupling coefficients. Each filter exhibits a continuously adjustable 3 dB bandwidth ranging from 2.25 GHz to 3.12 GHz, with an excellent 20 dB/3 dB shape factor of 3.08. This performance indicates significantly improved roll-off characteristics compared with the performance of traditional filter designs, leading to enhanced suppression of image frequency components and improved signal separation fidelity.</sec><sec>A complete microwave photon channelized receiving link is constructed using an integrated WDM-CROW filter bank. System-level simulations confirm that the architecture provides excellent broadband adaptability, supporting the channelization of radio frequency (RF) signals in two operational bands: 8–28 GHz and 8–36 GHz. The system efficiently decomposes the input wideband RF signal into eight independent intermediate frequency (IF) sub-bands. Within each sub-band, an image rejection ratio (IRR) exceeding 22 dB is maintained. The corresponding IF ranges are 1.4–3.6 GHz when configured for 8–28 GHz RF input, and 2–5 GHz for 8–36 GHz input, covering critical communication and detection bands from X-band to K-band and satisfying the requirements of multi-scenario signal processing. Furthermore, we simulate the reception and reconstruction of a 5 GHz bandwidth linear frequency-modulated (LFM) signal, successfully verifying the chip’s capability in handling wideband waveforms. These results underscore the feasibility of the proposed chip as a high-performance solution for advanced applications such as radar detection and broadband electronic warfare systems, offering a novel, integrated photonic alternative to traditional channelized reception architectures.</sec>

Design and performance analysis for adaptive detection of polarimetric radar in Gaussian environment and structured interference

This study introduces a portable radar system for real‐time monitoring of respiratory and heart rates in dairy cows. It uses millimeter‐wave Frequency Modulated Continuous Wave (FMCW) radar to perform noncontact physiological sensing, reducing behavioral disturbance. The radar system's design prioritizes portability, cost‐effectiveness, and robustness, allowing deployment in diverse farm environments. Experimental results show strong agreement between radar‐derived and reference measurements, confirmed through correlation analysis and supervised classification. Additionally, the study explores the integration of radar monitoring with advanced data analysis techniques, including Principal Component Analysis (PCA) and Support Vector Machines (SVM), to enhance livestock health management processes. The system is validated for its capability to monitor respiratory and heart rates in real time and effectively classify cows' reproductive states, achieving a classification accuracy of 79.63% for estrus detection. These findings demonstrate the feasibility of radar‐based physiological monitoring and support future integration with data‐driven management tools. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The increasing need for efficient perimeter defense has led to development of automated surveillance systems, though existing solutions are costly, computationally intensive, or require constant human monitoring. Many ultrasonic or radar-based systems also suffer from limited detection accuracy and lack real-time automated response. This study proposes a low-cost, Arduino-based automated radar detection and firing system integrating an ultrasonic sensor, dual servo motors, and a relay-controlled shooter. The system continuously scans the monitored area, detects intruders in real-time, aligns the shooter via servo motors, and executes an automatic firing response. Implemented using Embedded C on Arduino, experiments demonstrate detection accuracy exceeding 95%, improving over previous ultrasonic-only systems by approximately 20%. Results confirm reliable, autonomous monitoring and rapid response, offering a practical solution for military and defense application.

The large number of UAVs under supervision at low altitudes have brought serious security risks to the field of air defense. Accurately analyzing the characteristics of UAVs’ echo signals is of great research significance for the detection and recognition of UAVs. Based on the principle of radar detection, the echo spatial correlation in the distributed radar detection mode is studied. According to the influence of different movement speeds on the micro-motion characteristics of UAVs, the echo signal models of UAVs in two flight states are established. Combined with the instantaneous micro-Doppler frequency model of the ideal motion state of UAVs, micro-Doppler frequency calculation functions of UAVs at different attitude angles are constructed. Through simulation calculation, the variation curves between the observation angle and the echo spatial correlation using different detection distances are given. Based on time–frequency images of UAVs in their ideal motion state, changes in the time–frequency images at different motion speeds and attitude angles are analyzed. These research results will help radar detection systems to accurately recognize UAVs in an uncertain motion state and can also provide a basis for predicting the next motion action of UAVs in subsequent target tracking.

This study deals with the problem of enhancing aerial target detection for 3D radar. A novel approach which incorporates both signal and data processing is introduced. In order to increase the target’s SNR (signal-to-noise ratio), two consecutive transmit beams are used; for each, three beams are received simultaneously. All received beams are then processed. A two-stage constant false alarm rate (CFAR) algorithm is proposed for improving target detection. At the first-stage CFAR, the global CA-CFAR is applied to identify all possible target candidates (plots). Then, unsupervised machine learning is used to separate interference regions. For each interference region, the truncated probability density function of interference is estimated, and then a local CFAR (second-stage CFAR) is applied to reduce false plots while retaining target plots. The proposed approach is an extension of that given in recent publications. Tests on a 3D surveillance radar show the effectiveness of the proposed approach on aerial target detection in comparison with previous methods.

Detecting weak echoes from low-RCS targets in pulsed radar systems presents significant challenges, as conventional coherent accumulation methods require extended dwell times that reduce data rates and suffer from target-motion-induced migration. We propose RD-Transformer, an end-to-end attention-based architecture that reformulates coherent integration as a learned feature fusion problem. The framework integrates multi-pulse transpose preprocessing, dual-path self-attention encoders for transmitted and received signals, and a cross-attention decoder to extract transmit-receive interaction features. A tunable sigmoid-based gating mechanism enables flexible false alarm control during inference. Experiments on synthetic pulsed-radar data demonstrate that, under identical false alarm constraints (Pfa = 1 × 10−2 to 1 × 10−5) and using only 10 coherent pulses, RD-Transformer reduces the required SNR by 14–20 dB compared to optimal energy detection across Swerling I-IV target fluctuation models, validating the effectiveness of learned coherent accumulation for weak target detection.

ABSTRACT Compared to traditional radio frequency (RF) stealth techniques, which often rely on complex design or suffer from performance degradation due to active detection constraints, frequency diverse array (FDA) radar demonstrates superior RF stealth capabilities by dynamically adjusting frequency increments across array elements. Nevertheless, the existing RF stealth technologies do not provide a systematic modelling for the dynamic relationship between RF stealth and target active detection, making it insufficient to simultaneously evaluate radar active detection capability and RF stealth effectiveness. To address this issue, this study introduces a coupled analysis model, which establishes a link between active detection and RF stealth, utilising time-varying signals from FDA radar. Simulation results of different FDA signals with frequency-offset codings validate the effectiveness of the proposed model.

To address the challenges faced by traditional control methods in integrating the scheduling of detection and trajectory resources during the terminal guidance phase of hypersonic glide vehicles (HGVs), this paper proposes an intelligent joint optimization technique based on convex optimization pre-training. Using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, we introduce an adaptive detection-guidance weight distribution reward function. This design ensures that the vehicle meets guidance requirements during the terminal phase while allocating sufficient trajectory resources for radar detection. In interference suppression scenarios, the agent can avoid interference zones through trajectory resource scheduling and implement frequency hopping countermeasures via detection resource allocation. By jointly optimizing detection and trajectory resources, the method enhances terminal guidance confrontation performance. Additionally, to improve training efficiency, convex optimization-generated trajectory data is used for pre-training the agent, significantly reducing the convergence time. Simulation results show that this method effectively improves the performance of detection and counter-interference in the terminal guidance phase, providing new technical means for intelligent guidance in complex battlefield environments.

Orbital angular momentum (OAM) vortex waves have become a prominent topic in scientific research and engineering applications. With the developments of metasurface-based OAM generation and manipulation technologies, Huygens' metasurfaces demonstrate the advantages of low profile and simple structure. In this paper, we propose a broadband Huygens' metasurface capable of polarization-independent OAM-multiplexed beam deflections. Firstly, a highly efficient polarization-independent Huygens' meta-atom is designed, and then the dual-polarized 3-bit phase profiles are constructed to generate the polarization-independent OAM-multiplexed beams. On this basis, dual-polarized phase sequences are incorporated into the 3-bit phase profiles, enabling the broadband polarization-independent OAM-multiplexed dual beam deflections in two orthogonal planes. We fabricated and measured three metasurface samples, and the experimental results are consistent with the simulations, confirming the feasibility and practicality of the proposed method. The proposed metasurface operated at 8.5 GHz-11.5 GHz, achieving a 30% relative bandwidth and 14.8% aperture efficiency. We believe this design demonstrates promising potential for applications in multi-target wireless communications and wide-range radar detections.

Small and medium Unmanned Aerial Vehicles (UAVs) are commonly equipped with diverse sensors for situational awareness, including cameras, Frequency-Modulated Continuous-Wave (FMCW) radars, Light Detection and Ranging (LiDAR) systems, and ultrasonic sensors. However, optical systems are constrained by adverse weather and darkness, while the limited detection range of compact FMCW radars-typically a few hundred meters-is often insufficient for higher-speed UAVs, particularly those operating Beyond Visual Line of Sight (BVLOS). This paper presents a Collision Avoidance System (CAS) based on a lightweight pulse radar, targeting medium UAV platforms (10–300 kg MTOM) where installing large, nose-mounted radars is impractical. The system is designed for obstacle detection at ranges of 1–3 km, directly addressing the standoff distance limitations of conventional sensors. Beyond its primary sensing function, the pulse architecture offers several operational advantages. Its lower time-averaged power also results in a reduced electromagnetic footprint, mitigating interference and supporting emission-control objectives. Furthermore, pulse radar offers greater robustness against interference in dense electromagnetic environments and lower power consumption, both of which directly enhance UAV operational endurance. Field tests demonstrated a one-to-one correspondence between visually identified objects and radar detections across 1–3 km, with = 1.5%, confirming adequate standoff for tens of seconds of maneuvering time, with range resolution of 3.75 m and average system power below 80 W.

Core-hollow cavity-shell ternary MOF-derived composites with hierarchical heterointerfaces enable ultra-broadband electromagnetic wave absorption via multiscale loss synergy.

BCA-DetNet: Background Contrast-Based Attention Detection Network for UAVs Detection in Pulse-Doppler Radar

Photonic-assisted radar for simultaneous detection of velocity, distance and angle of arrival of multiple targets

Stealth technology plays a vital role in reducing the radar detectability of aircraft and other defense platforms. This review focuses on the development and performance of carbon-based radar-absorbing materials (RAMs) and their contribution to electromagnetic and infrared stealth applications. Research articles published between 2010 and 2025 were collected from Web of Science, Scopus, and ScienceDirect databases using keywords such as carbon-based RAM, graphene radar absorber, carbon nanotube composites, and low-infrared emissivity coatings. The selected studies were evaluated based on absorption efficiency, bandwidth, impedance matching, coating thickness, and thermal – environmental stability. Special attention is given to the role of carbon nanotubes (CNTs), graphene, carbon fibers, and carbon black as fillers in polymer matrices and multilayer configurations that enhance electromagnetic attenuation through dielectric, magnetic, and interfacial polarization mechanisms. The review also highlights key challenges such as large-scale fabrication, spectral selectivity, and durability under operational conditions. By summarizing experimental trends and recent advances, this study provides a focused and up-to-date understanding of carbon-based composites for next-generation stealth and low-emissivity applications.

Using GNSS (Global Navigation Satellite System) signal as signal source and applying the principle of radar for object detection is a non-contact and emerging object detection technology. However, due to the problems that weak signal and low resolution, the application of such technology in object detection is very limited. On the basis of the detection of moving targets in the air, this paper puts forward a SNR (signal-to-noise ratio) improvement scheme of RDM (range doppler map). In this paper, bi-static GNSS radar model is employed. The RDM of air target is obtained on the basis of direct and reflected signals. The proposed RDM SNR improvement algorithm mainly by successively deducting the noise term in GNSS signals, so as to improve resolution of RDM and accuracy of target motion state solution. Compared with the current noise reduction method, which mostly depends on the prior conditions such as background environment, this algorithm is more applicable, as it is mainly based on the characteristics of the signal. The RDM spectrum is obtained by simulating moving target detection. The algorithm shows that the image information entropy after noise reduction is 63.23% for GPS signal and 71.69% for Beidou signal, which lower than that before noise reduction.

This paper provides a comprehensive review of five core amplifier architecture: Cascode, Cascade, Gain Boosting, Current Cancellation, and Bootstrapping, which aims at breaking through the limitation of Gain-Bandwidth Product in modern electronical systems. Facing the demands of 5G/6G mobile communication, radar detection and medical imaging, this study systematically analyzes the principle, structure and performance of multiple techniques, clarifying its improvement effect on aspects such as gain, bandwidth, output impedance and stability. A comparative assessment of these architectures reveals their respective advantages in different application scenarios and process conditions. Furthermore, the paper also indicates that combining these circuits with FinFET and three-dimensional heterogeneous integration, will emphasize their combined potential to significantly boost linearity, noise performance, and power efficiency. The study also addresses ongoing challenges and future directions in implementing these techniques in mass production environments. These advancements offer critical support significant potential for next-generation high-frequency and high-precision amplifier designs, paving the way for more efficient and reliable electronic systems in emerging technological domains.

ABSTRACT Partially biodegradable, toughened, electromagnetic absorbing materials were developed using poly(lactic acid) (PLA), liquid isoprene rubber (LIR) and graphene nanoplatelets (GNP). The materials were evaluated for their morphology, tensile properties, rheological behavior, crystallization characteristics, electrical conductivity and electromagnetic absorptivity. GNP not only reduced the interfacial tension between PLA and LIR, but also acted as a nucleating agent. The incorporation of LIR and LIR/GNP (5 phr GNP) significantly improved the toughness of the PLA matrix. The electrical percolation threshold for the PLA/LIR/GNP composite was determined to be 3.02 vol% GNP. Electromagnetic absorption was studied in both monolayer (bulk) and bilayer configurations, with layers of identical composition. The bulk materials showed optimal absorption performance at a thickness of 2 mm, achieving a minimum reflection loss (RL) of −25 dB and an effective absorption bandwidth (EAB) below −10 dB spanning 5.12 GHz, with 15 phr GNP. Bilayer samples, particularly those containing more than 7 phr GNP, exhibited broader EABs compared to their bulk counterparts. In conclusion, the synergistic combination of LIR and GNP enhanced the mechanical, electrical and electromagnetic properties of PLA, making these composites promising candidates for use in electromagnetic shielding for electronic packaging and stealth technology aimed at reducing radar detectability.

Abstract The rapid advancement of radar detection and communication technologies, coupled with the increasing complexity of electromagnetic (EM) environments, has driven the demand for multispectral‐compatible stealth materials that can function across microwave (MW), terahertz (THz), and infrared (IR) bands. This review systematically summarizes the recent progress in the design and development of ultrabroadband EM waves (EMWs) stealth materials. First, it clarifies the fundamental principle of achieving single‐spectrum stealth and the inherent challenges of multi‐spectrum compatibility, especially the contradictory requirements in different frequency bands. Designing multispectral compatible stealth materials requires a rational design of the multi‐scale structure and composition at both macro/micro level and atomic/molecular level. Subsequently, an analysis of the state‐of‐the‐art materials and structures, such as aerogels, multilayer composites, and macroscopic metamaterials, is carried out. Particular emphasis is placed on heterogeneous interface engineering, gradient design, bioinspired concepts, and intelligent responsive systems. Ultimately, potential solutions, including deepening the understanding of the loss mechanism and machine learning‐assisted material design, are presented to develop multifunctional, adaptive stealth systems. This review aims to provide valuable insights and inspiration to researchers in next‐generation multispectral‐compatible stealth technologies.

Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). We will release the RoadsideRadar dataset along with the code implementation of this work at https://github.com/bhanderisavan/roadside-radar-seg.