Search Results (67)

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article

Compact, broadband, multi-channel RF chips with low loss and high integration are required for high-performance phased-array systems. Presented in this paper is a four-channel, multifunction RF chip operating in the 5–18 GHz frequency range that integrates broadband phase shifting, amplitude control, power amplification, and switching functions. The chip is designed to have flip-chip bonding and stacked gold bumps to enable the compact 3D integration of the GaAs pHEMT and Si-CMOS. To ensure high-density interconnects with minimal parasitic effects, a fan-in redistribution process is implemented. The RF front-end part of this chip, fabricated through a 0.15 µm GaAs pHEMT process, integrates 6-bit digital phase shifters, 6-bit digital attenuators, low-noise amplifiers (LNAs), power amplifiers (PAs), and single-pole double-throw (SPDT) switches. To enhance multi-channel isolation and reduce crosstalk between RF chips and digital circuits, high isolation techniques, including a ground-coupled shield layer in the fan-in process and on-chip shield cavities, are utilized, which achieve isolation levels greater than 41 dB between adjacent RF channels. The measurement results demonstrate a reception gain of 0 dB with ±0.6 dB flatness, an NF below 11 dB, and transmit gain of more than 10 dB, with a VSWR of below 1.6 over the entire 5–18 GHz frequency band. The 6-bit phase shifter achieves a root mean square (RMS) phase error below 2.5° with an amplitude variation of less than 0.8 dB, while the 6-bit attenuator exhibits an RMS attenuation error of below 0.5 dB and a phase variation of less than 7°. The RF and digital chips are heterogeneously integrated using flip-chip and fan-in technology, resulting in a compact chip size of 6.2 × 6.2 × 0.33 mm³. These results validate that this is a compact, high-performance solution for advanced phased-array radar applications. Full article

With the increasing complexity of the electromagnetic environment, radar waveform design needs to break through the limitation of traditional single-function architectures, prompting the emergence of integrated radar waveforms. Currently, the mainstream integrated signals are achieved through conventional waveform synthesis or time/frequency division multiplexing. However, the former suffers from limited design flexibility and is confined to single scenario applications, while the latter has interference between different sub-channels, which will limit the performance of multi-function radar. Aiming at the above problems, this paper proposes a waveform optimization method for a detection and cover integrated signal with high Doppler tolerance. By constructing a joint optimization model, the sidelobe characteristics of the signal’s autoambiguity function and the output response of the non-cooperative matched filter were incorporated into the unified objective function framework. The gradient descent algorithm is used to solve the model, and the optimized waveform with low sidelobe characteristics and multiple false target interference abilities is obtained. When the optimized waveform generates multiple false targets to cover our radar position, its peak sidelobe level (PSL) drops below −23 dB, and most of the sidelobe levels in the range-Doppler interval of interest drop below −40 dB. Finally, the proposed integrated waveform undergoes hardware-in-the-loop experiments, experimentally validating its performance and the effectiveness of the proposed method. Full article

Multi-function radar (MFR) work modes recognition is an important research component of the electronic reconnaissance field. When facing MFR systems equipped with complex mode-waveform mapping relationships and flexible beam scanning techniques, the intercepted work mode pulse sequences have a wide temporal range of feature distributions and variable durations, which bring significant challenges for accurate recognition. To address this issue, this study constructs a novel hierarchical MFR signal model with waveform multiplexing and waveform scheduling laws with spatial beam arrangement and proposes a work mode recognition method based on dual-scale feature extraction. The recognition method first obtains the variable-length sequence processing capability through pulse sequence segmentation. Then, a structure composed of convolutional neural network (CNN) and long short-term memory (LSTM) is followed to extract the deep time-series features at the internal-segment scale of segments, and the features of each segment are concatenated in the time dimension. Subsequently, an LSTM-Attention network is used to extract the external-segment-scale features while adaptively assigning a higher weight to important waveform segments. Ultimately, the work mode recognition results are obtained. The experimental results show that the proposed method’s performance is advantageous in recognizing work modes under the comprehensive MFR signal model. Full article

We demonstrate a thermally controlled ultra-wideband wide incident angle metamaterial absorber with switchable transmission at the THz band in this paper. The underlying hybrid structure of FSS-VO₂ thin films make them switchable between absorption mode and transmission mode by controlling the temperature. It can achieve ultra-wideband absorption with above 90% absorption from 1 THz to 10 THz and exhibits excellent absorption performance under a wide range of incident and polarization angles at a high temperature (80 °C). At room temperature (27 °C), it acts in transmission mode with a transmission coefficient of up to 60% at 3.1278 THz. The transmission region is inside the absorption band, which is very important for practical applications. The metamaterial absorber has the advantage of easy fabrication, an ultra-wideband, a wide incident angle, switchable multi-functions, and passivity with wide application prospects on terahertz communication and radar devices. Full article

This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. Specifically, for the aforementioned complex deployment regions, a region decomposition approach based on convex partitioning is proposed. This approach allows for the decomposition of complex regions into multiple non-overlapping convex subregions. Moreover, for convex deployment regions or subregions, we propose a coordinate transformation approach to eliminate the constraints introduced by the shape of the convex region. By combining the above approaches, we introduce a novel MOPSO based on decomposition and transformation, named MOPSO-DT. This algorithm aims to optimize MFRN deployment in these challenging environments. Experimental results demonstrate the superiority of the MOPSO-DT algorithm over two existing algorithms across a variety of deployment cases, highlighting its enhanced efficiency, effectiveness, and stability. These findings indicate that the proposed algorithm is well suited for optimizing MFRN deployment in complex, irregular regions, offering significant improvements in performance compared to conventional methods. Full article

With advancements in phased array and cognitive technologies, the adaptability of modern multifunction radars (MFRs) has significantly improved, enabling greater flexibility in waveform parameters and beam scheduling. However, these enhancements have made it increasingly difficult to establish fixed relationships between working modes using traditional radar recognition methods. Furthermore, conventional approaches often exhibit limited robustness and computational efficiency in complex or noisy environments. To address these challenges, this paper proposes a joint learning framework based on a hybrid model combining convolutional neural networks (CNNs) and Transformers for MFR working mode recognition. This hybrid model leverages the local convolution operations of the CNN module to extract local characters from radar pulse sequences, capturing the dynamic patterns of radar waveforms across different modes. Simultaneously, the multi-head attention mechanism in the Transformer module models long-range dependencies within the sequences, capturing the “semantic information” of waveform scheduling intrinsic to MFR behavior. By integrating characters across multiple levels, the hybrid model effectively recognizes MFR working modes. This study used the data of the Mercury MFR for modeling and simulation, and proved through a large number of experiments that the proposed hybrid model can achieve robust and reliable identification of advanced MFR working modes even in complex electromagnetic environments. Full article

A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s). It is demonstrated that the FPGA data-processing speed matches that of the radar output, thus eliminating data loss. The radar system achieves a remarkable speed of over 9000 waveforms per second on each channel. The proposed architecture is scalable to accommodate higher sampling rates and various waveform periods. It is also multi-functional since the FPGA controls and synchronizes two transmitters and a dual-channel receiver, performs signal reconstruction on both channels simultaneously, and carries out user-defined averaging, trace windowing, and interference suppression for improving the receiver’s signal-to-noise ratio. We also investigate the throughput rate while offloading radar data onto an external device through an Ethernet link. Since the radar data rate significantly exceeds the Ethernet link capacity, we show how the FPGA-based averaging and windowing functions are leveraged to reduce the amount of offloaded data while fully utilizing the radar output. Full article

Microwave absorbers with infrared camouflage are highly desirable in military fields. Self-supporting 3D architectures with tailorable shapes, composed of FeCoNi alloy/carbon nanotubes (CNTs) @ carbon nanofibers (CNFs), were fabricated in this study. On the one hand, multiple loss mechanisms were introduced into the high-elastic sponges. Controllable space conductive networks caused by the in situ growth of CNTs on the CNFs contributed to the effective dielectric and resistance loss. Moreover, the uniformly distributed magnetic alloy nanoparticles (NPs) with dense magnetic coupling resulted in magnetic loss. On the other hand, heterogeneous interfaces were constructed by multicomponent engineering, causing interfacial polarization and polarization loss. Furthermore, the internal structures of sponges were optimized by regulating the alloy NPs sizes and the growth state of CNTs, then tuning the impedance matching and microwave absorption. Therefore, the high-elastic sponges with ultra-low density (7.6 mg·cm⁻³) were found to have excellent radar and infrared-compatible stealth properties, displaying a minimum refection loss (RL_min) of −50.5 dB and a maximum effective absorption bandwidth (EAB_max) of 5.36 GHz. Moreover, the radar stealth effect of the sponges was evaluated by radar cross-section (RCS) simulation, revealing that the multifunctional sponges have a promising prospect in military applications. Full article

Multifunctional structures with excellent wave-absorbing and load-bearing properties have attracted much attention in recent years. Unlike other wave-absorbing materials, honeycomb wave-absorbing materials have appealing radar absorption and mechanical properties. However, the existing honeycomb wave-absorbing materials have problems such as narrow absorption band and poor compression resistance. In this study, a novel frequency selective surface-enhanced composite honeycomb absorbers (FSS-CHAs) are fabricated by combining a honeycomb structure with wonderful load-bearing capacity and FSS through screen-printing and inlay-locking techniques. After reflectivity measurements, the effective absorption band (RL < −10 dB) of CHA is 6.25–17.47 GHz and a bandwidth of 11.22 GHz, the effective absorption band of the FSS-CHA is 3.96–18 GHz and a bandwidth of 14.04 GHz, 25.13% improvement compared to the CHA, the mechanism of wave absorption is explained using transmission line theory. The simulation results show that the wide bandwidth is due to the different absorption mechanisms of FSS-CHA at low and high frequencies. The compression test shows that the compression strength of FSS-CHA is 17.10 MPa. In addition, FSS-CHA has a low cost of only USD 270.7/m². This study confirms the possibility of combining FSS with radar-absorbing honeycombs, which provides a reference for the design of future broadband wave-absorbing structures, offers a novel approach to integrating FSS with CHA, and aims to optimize their efficacy and utility in stealth technology. Full article

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of $[-50,50]$ m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications. Full article

Polyurethane (PU) foams are cellular polymeric materials that have attracted much attention across various industries because of their versatile properties and potential for multifunctional applications. PU foams are involved in many innovations, especially in multi-functional and high-performance applications. Special attention is given to developing tailored PU foams for specific application needs. These foams have various applications including flame retardancy, sound absorption, radar absorption, EMI shielding, shape memory, and biomedical applications. The increasing demand for materials that can perform multiple functions while maintaining or enhancing their core properties has made PU foams a focal point of interest for engineers and researchers. This paper examines the challenges faced by the PU foam industry, particularly in developing multifunctional products, as well as the strategies for improving sustainability, such as producing PU foams from renewable resources and recycling existing materials. Full article

The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset. As a transitional phase towards Cognitive Multi-Functional Radar (CMFR), Adaptive Multi-Function Radar (AMFR) possesses the capability to emit identical waveform signals across different working modes and states for task completion, with dynamically adjustable waveform parameters that adapt based on scene information. From a reconnaissance perspective, the valid signals received exhibit sparsity and localization in the time series. To address this challenge, we have redefined the reconnaissance-focused research priorities for radar systems to emphasize behavior analysis instead of pattern recognition. Based on our initial comprehensive digital system simulation model of a radar, we conducted reconnaissance and analysis from the perspective of the reconnaissance side, integrating both radar and reconnaissance aspects into environmental simulations to analyze radar behavior under realistic scenarios. Within the system, waveform parameters on the radar side vary according to unified rules, while resource management and task scheduling switch based on operational mechanisms. The target in the reconnaissance side maneuvers following authentic behavioral patterns while adjusting the electromagnetic space complexity in the environmental aspect as required. The simulation results indicate that temporal annotations in signal flow data play a crucial role in behavioral analysis from a reconnaissance perspective. This provides valuable insights for future radar behavior analysis incorporating temporal correlations and sequential dependencies. Full article

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models. Full article

In order to implement multiple electromagnetic (EM) wave front control, a reconfigurable multifunctional metasurface (RMM) has been investigated in this paper. It can meet the requirements for 6G communication systems. Considering the full-space working modes simultaneously, both reflection and transmission modes, the flexible transmission-reflection-integrated RMM with p-i-n diodes and anisotropic structures is proposed. By introducing a 45°-inclined H-shaped AS and grating-like micro-structure, the polarization conversion of linear to circular polarization (LP-to-CP) is achieved with good angular stability, in the transmission mode from top to bottom. Meanwhile, reflection beam patterns can be tuned by switching four p-i-n diodes to achieve a 1-bit reflection phase, which are embedded in the bottom of unit cells. To demonstrate the multiple reconfigurable abilities of RMMs to regulate EM waves, the RMMs working in polarization conversion mode, transmitted mode, reflected mode, and transmission-reflection-integrated mode are designed and simulated. Furthermore, by encoding two proper reflection sequences with $13\times 13$ elements, reflection beam patterns with two beams and four beams can be achieved, respectively. The simulation results are consistent with the theoretical method. The suggested metasurface is helpful for radar and wireless communications because of its compact size, simple construction, angular stability, and multi-functionality. Full article