Search Results (213)

In this study, an accurate attenuation measurement system with high attenuation capability (≥100 dB) is presented, covering a broad radio frequency range from 1 GHz to 25 GHz. The system employs a dual-channel intermediate frequency (IF) substitution method, utilizing a programmable inductive voltage divider (IVD) that provides precise voltage ratios at a 1 kHz operating IF, serving as the primary attenuation standard. To ensure optimal inter-channel isolation, essential for accurate high-attenuation measurements, an optical fiber assembly, consisting of a laser diode, a wideband external electro-optic modulator, and a photodetector, is integrated between the channels. A comprehensive performance evaluation is presented, with particular emphasis on the programmable IVD calibration technique, which achieves an accuracy better than 0.001 dB across all attenuation levels, and on the role of the optical fiber assembly in enhancing isolation, demonstrating levels exceeding 120 dB across the entire frequency range. The system demonstrates measurement capabilities with expanded uncertainties (k = 2) of 0.004 dB, 0.008 dB, and 0.010 dB at attenuation levels of 20 dB, 60 dB, and 100 dB, respectively. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

In this paper, RF sub-modules with millimeter-wave functionality are considered and verified for designing an ultra-wideband receiver (18–40 GHz) required in the electronic support measure (ESM) field. The pre-design of an ultra-wideband super heterodyne receiver (SHR) requires a front-end module (FEM) with four units in the system. Each FEM has four channels with the same path, while the quadrature millimeter down-converter (QMDC) needs to have a converting function that uses a broadband mixer. The FEM includes the ability to provide built-in test (BIT) path functionality to the antenna ports prior to system field installation. Each path of the QMDC requires the consideration of several factors, such as down-converting, broadband gain flatness, and high isolation. As this is an RF module requiring high frequency and wideband characteristics, it is necessary to identify risk factors in advance within a predictable range. Accordingly, the blind-mate A (BMA) connector connection method, the phase-alignment test method in the down-conversion structure, and the LO signal, IF path inflow-blocking method were analyzed and designed. Full article

The inherent non-ideal characteristics of circuit components and inter-channel mismatch errors induce nonlinear amplitude and phase distortions in time-interleaved pipelined analog-to-digital converters (TI-pipelined ADCs), significantly degrading system performance. Limited by prior modeling, conventional digital calibration methods only correct partial errors, while machine learning (ML) approaches achieve comprehensive calibration at a high computational cost. This work proposes an ensemble calibration framework that combines polynomial modeling and ML techniques. The ensemble calibration framework employs a two-stage correction: a learned Volterra front-end performs forward mapping to compensate static baseline nonlinear distortions, while a lightweight neural network back-end implements inverse mapping to correct dynamic nonlinear distortions and inter-channel mismatch errors adaptively. Experiments conducted on TI-pipelined ADCs show improvements in both the spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratio (SNDR). It is noteworthy that in two ADCs fabricated using 40 nm CMOS technology, the 12-bit, 3000 MS/s silicon-validated four-channel TI-pipelined ADC exhibits SFDR and SNDR improvements from 35.47 dB and 35.35 dB to 79.70 dB and 55.63 dB, respectively, while the 16-bit, 1000 MS/s silicon-validated four-channel TI-pipelined ADC demonstrates an enhancement from 38.62 dB and 40.21 dB to 80.90 dB and 62.43 dB, respectively. Furthermore, a comparison with related studies reveals that our method achieves comprehensive calibration performance for wide-band inputs while substantially reducing computational complexity, requiring only 4.4 K parameters and 8.57 M floating-point operations per second (FLOPs). Full article

The spectrogram-based wideband signal detection framework has garnered increasing attention in various wireless communication applications. However, the front-end spectrograms in existing methods suffer from visual and informational deficiencies. This paper proposes a novel multichannel enhanced spectrogram (MCE spectrogram) to address these issues. The MCE spectrogram leverages additional channels for both visual and informational enhancement, highlighting signal regions and features while integrating richer recognition information across channels, thereby significantly improving feature extraction efficiency. Moreover, the back-end networks in existing methods are typically transferred from original object detection networks. Wideband signal detection, however, exhibits task-specific characteristics, such as the inherent signal-to-noise ratio (SNR) attribute of the spectrogram and the large variations in shapes of signal bounding boxes. These characteristics lead to issues like inefficient task adaptation and anchor mismatch, resulting in suboptimal performance. To tackle these challenges, we propose an SNR-aware detection network that employs an anchor-free paradigm instead of anchors for signal detection. Additionally, to address the impact of the SNR attribute, we design a trainable gating module for efficient feature fusion and introduce an auxiliary task branch to enable the network to capture more discriminative feature representations under varying SNRs. Experimental results demonstrate the superiority of the MCE spectrogram compared to those utilized in existing methods and the state-of-the-art performance of our SNR-aware Net among comparable detection networks. Full article

For a bottom-moored vertical line array in deep ocean, the underwater maneuvering source will produce interference patterns in both grazing angle–distance (vertical-time record, VTR) and frequency–grazing angle (wideband beamforming output) domains, respectively, and the interference period is modulated by the source depth. Based on these characteristics, an interference feature fusion (IFF) method is proposed in the space–time–frequency domain for source depth estimation, in which the principal interference mode of the VTR is extracted adaptively and the depth ambiguity function is constructed by fusing the ambiguity sequence, mapped by wideband beamforming intensity, and the principal interference mode, which can achieve the long-term depth estimation and recognition of underwater sources without requiring environmental information. Theoretical analysis and simulation results indicate that the IFF can suppress the false peaks generated by the generalized Fourier transform (GFT) method, and the depth estimation error of the IFF for a single source is reduced by at least 47% compared to GFT. In addition, the IFF is proven to be effective at separating the depth of multiple adjacent sources (with the average estimation error reduced by 28%) and exhibits a high degree of robustness within the fluctuating acoustic channel (with the average estimation error reduced by 12%). Full article

With the continuous expansion of communication bandwidth, accurately modeling the non-linear characteristics of power amplifiers has become increasingly challenging, directly affecting the performance of digital pre-distortion (DPD) technology. The high peak-to-average power ratio and complex modulation schemes of wideband signals further exacerbate the difficulty of DPD implementation, necessitating more efficient algorithms. To address these challenges, this paper proposes a wideband DPD algorithm based on edge signal correction. By acquiring signals near the center frequency and comparing them with equally band-limited feedback signals, the algorithm effectively reduces the required processing bandwidth. The incorporation of cross-terms for model calibration enhances the model fitting accuracy, leading to significant improvement in pre-distortion performance. Simulation results demonstrate that compared with traditional DPD algorithms, the proposed method reduces the error vector magnitude (EVM) from 1.112% to 0.512%. Experimental validation shows an average improvement of 11.75 dBm in adjacent channel power at a 2 MHz frequency offset compared to conventional memory polynomial DPD. These improvements provide a novel solution for power amplifier linearization in wideband communication systems. Full article

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case $12\%$ margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems. Full article

The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework’s flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa. Full article

In order to realize robust communication in complicated interference electromagnetic environments, an intelligent transmitter design is proposed in this paper, where an auxiliary wideband receiver senses the electromagnetic distribution information in a wide bandwidth range to decide the optimal working frequency. One of the key issues is suppressing the self-interference of high-power transmitter signals to the co-platform wideband sensing receiver. Due to the multipath effect of the self-interference channel, perfect time synchronization of self-interference signals is not achievable, which reduces the interference cancelation performance of the co-platform. Therefore, this paper investigates the impact of time synchronization errors on the self-interference cancellation performance of the Nyquist folding receiver (NYFR)-based system. First, a self-interference cancellation architecture based on NYFR is proposed to support the realization of real-time wideband spectrum sensing. Secondly, closed-form expressions for the residual interference power and the self-interference cancellation performance are derived, and the impact of reference signal sampling errors on the self-interference cancellation performance is also analyzed. Theoretical analysis and simulation results show that the NYFR-based self-interference cancellation performance decreases with increasing time synchronization errors and folding multiples, and the system is especially sensitive to time synchronization errors. Moreover, frequency detection simulations show that, under an SI-to-NCS power ratio of 0 dB, the proposed interference cancellation scheme improves the frequency detection probability by approximately 80%. The research results provide a theoretical reference for the compressed sensing-aided intelligent transmitter realization. Full article

In this study, we propose a method to estimate the Angle-of-Arrival (AoA) of OFDM-based drone signals with wideband and burst characteristics using only a single-channel receiver and a switched-beam antenna. First, six circularly arranged directional antennas are time-division controlled using RF switches to measure the received power of each antenna. Next, the maximum beam pattern and the measured power of each antenna are synthesized in vector form, and the direction of the synthesized vector becomes the angle of arrival of the drone signal. To verify the proposed method, an experiment was conducted using the video signal of DJI Phantom 4 Pro with a bandwidth of 10 MHz. As a result, it was confirmed that stable angle-of-arrival estimation of drone video signals was possible with an average error of less than 5°. The proposed system has the advantage of being able to estimate the AoA of a drone with only a single receiver without the need for synchronization. Therefore, the proposed system is expected to be used as a low-cost, compact, and highly portable anti-drone system. Full article

This paper presents an optimized long-wave (10–300 kHz) wideband direction-finding system for scientific research. The antenna unit of the system comprises one vertical electric field sensor and two horizontal magnetic field sensors oriented in the north–south and east–west directions, respectively. The overall design prioritizes compactness, engineering feasibility, and ease of deployment, enabling the effective reception of long-wave radio signals within the 10–300 kHz range. The magnetic field sensitivity reaches $8fT/\sqrt{\mathrm{Hz}}@10\mathrm{kHz}$, while the electric field sensitivity achieves $3.2\mathsf{\mu }\mathrm{V}/\mathrm{m}/\sqrt{\mathrm{Hz}}@10\mathrm{kHz}$. The overall sensitivity of the receiver is $1\mathsf{\mu }\mathrm{V}$ (300 Hz bandwidth, 10 dB signal-to-noise ratio). The synchronization accuracy of the system is within 10 ns. Theoretically, with a baseline length of 5 km and a signal incidence angle ranging from 9.9° to 170.1°, the direction finding error is less than 2°. Additionally, direction-finding methods for MSK and ASK modulated signals are analyzed. To evaluate the system’s actual performance, initial measurements were conducted in Qingdao, Shandong. Full article

This paper proposes an N-path notch filter while offering high blocker power handling for co-address transmitter (TX) leakage suppression in a wideband receiver. The filter includes an impedance flip-flop over an adjustable transmission line in a block band, achieving a 12.3 to 13.6 dBm blocker 1 dB compression point (B1dB) and a 12.8 to 14 dBm 1 dB compression point (P1dB) in a 130 nm CMOS SOI process. This design effectively suppresses broadband interference in the receiving system and improves the dynamic range and linearity of the receiver (RX) channel. The filter consumes 186 to 242 mW in the 0.3 GHz to 0.6 GHz band and has an active chip area of 0.21 mm², providing maximum rejection >25 dB, with a passband third-order input intercept point (IIP3) of 22 to 25.2 dBm. The design of the adjustable transmission line structure is analyzed to reduce the insertion loss in terms of impedance and to achieve a 1.7 to 2 dB insertion loss shortfall over the RF tuning range. Full article

In complex electromagnetic environments, such as substations, converter stations in power systems, and the compartments of aircraft, trains, and automobiles, electromagnetic immunity testing is crucial. It requires that the electric field sensor has features such as a large dynamic measurement range (amplitude from hundreds of V/m to tens of kV/m), a fast response speed (response time in the order of nanoseconds or sub-nanoseconds), a wide test bandwidth (DC to 1 GHz even above), miniaturization, and robustness to strong electromagnetic interference. This paper introduces a multi-channel, ultra-wideband transient electric field measurement system. The system’s analog bandwidth covers the spectrum from DC and a power frequency of 50 Hz to partial discharge signals, from DC to 1.65 GHz, with a storage depth of 2 GB (expandable). It overcomes issues related to the instability, insufficient bandwidth, and lack of accuracy of optical fibers in analog signal transmission by using front-end digital sampling based on field-programmable gate array (FPGA) technology and transmitting digital signals via optical fibers. This approach is effectively applicable to measurements in strong electromagnetic environments. Additionally, the system can simultaneously access four channels of signals, with synchronization timing reaching 300 picoseconds, can be connected to voltage and current sensors simultaneously, and the front-end sensor can be flexibly replaced. The performance of the system is verified by means of a disconnect switch operation and steady state test in an HVDC converter station. It is effectively applicable in scenarios such as the online monitoring of transient electromagnetic environments in high-voltage power equipment, fault diagnosis, and the precise localization of radiation sources such as partial discharge or intentional electromagnetic interference (IEMI). Full article