Search Results (33)

Ultra-wide swath synthetic aperture radar (SAR) systems are of great significance for applications such as terrain measurement and ocean monitoring. In conventional SAR systems, targets echo from the near-range and far-range of an observed swath mutually overlap, and the blind ranges are caused by those that the radar cannot receive while it is transmitting. Therefore, the swath of conventional SAR systems is limited due to their range ambiguity as well as the transmitted pulse blockage. With the development of waveform diversity, range ambiguity can be suppressed by radar waveform design with a low-range sidelobe without increasing the system’s complexity when compared to the scan-on-receive (SCORE) based on digital beamforming (DBF) technique. Additionally, by optimizing the pulse repetition interval (PRI) variation strategy, the negative impact of blind range on imaging can be reduced. Based on these technologies, this paper proposes a theoretical architecture to achieve an ultra-wide swath SAR imaging system via chaotic frequency modulation (FM) signals and a random pulse repetition interval variation strategy without increasing the antenna area. By transmitting time-variant chaotic-FM signals, the interference between targets in the near range and far range can be reduced by the corresponding match filters. Furthermore, random pulse repetition intervals increase the irregularity and aperiodicity of the blind ranges to further improve the imaging quality. Simulation results demonstrate that the proposed wide-swath SAR system has better performance compared to classical SAR systems. Full article

Existing covert communication schemes based on On–Off Keying (OOK) have not considered asymmetric noise environments, which limits their applicability in complex communication scenarios such as terahertz and underwater acoustic covert communications. To address this issue, this paper proposes a phase-based OOK coding scheme. In particular, the transmitter Alice can adjust the initial phase of the transmitted symbol to align the signal with the stronger noise components in asymmetric noise communication scenarios, thereby exploiting the masking effect of noise to achieve covert transmission. To quantify performance, the KL divergence and mutual information of the OOK coding scheme are adopted as measures of covertness and transmission performance, respectively. An optimization problem involving the input signal distribution $a_n$, signal amplitude $\beta$, and initial phase angle $\theta$ is formulated and solved to obtain the maximum covert transmission rate. Numerical results demonstrate that in asymmetric noise systems, the initial phase angle and the Gaussian noise components on the real and imaginary axes of the complex plane influence both covertness performance and transmission rate. Adjusting the initial phase towards the direction with lower noise components can maximally suppress noise interference, thereby improving the covertness performance. Full article

This paper presents a novel ultrathin frequency selective surface (FSS) bandpass filter with an extraordinary wideband tailored for operating within the S-C bands. The filter structure entails a double-layer FSS structure with mutually perpendicular unit cells etched on the top and bottom sides of a 0.003${\lambda }_L$ thick FR4 dielectric substrate, where ${\lambda }_L$ is the free space wavelength at the lowest operating frequency. Thus, both TE and TM polarizations can be covered, ensuring the polarization insensitivity of the structure. The two FSS layers are loaded with resistors to implement the harmonic suppression principle. The overall periodicity is extremely compact, measuring 0.16${\lambda }_L$× 0.16${\lambda }_L$. An equivalent circuit analysis was conducted to comprehensively evaluate the structure and provide design guidelines. Numerical simulations and experimental measurements demonstrated that the proposed filter achieved a −3 dB transmission band spanning from 2 to 6.76 GHz (fractional bandwidth equal to 108.7%) under normal incidence. Moreover, aside from excellent wideband performance, the filter showcased a flat bandpass and stable responses up to 40° of incidence angle. These remarkable capabilities position the proposed filter as a valuable asset in advancing the development of radomes and applications relevant to electromagnetic interference (EMI) shielding, promising significant contributions to the field. Full article

As the number of devices using wireless communications increases, the amount of usable radio frequency spectrum becomes increasingly congested. As a result, the need for robust, adaptive communications to improve spectral efficiency and ensure reliable communication in the presence of interference is apparent. One solution is using beamforming techniques on digital phased-array receivers to maximize the energy in a desired direction and steer nulls to remove interference; however, traditional phased-array beamforming techniques used for interference removal rely on perfect calibration between antenna elements and precise knowledge of the array configuration. Consequently, if the exact array configuration is not known (unknown or imperfect assumption of element locations, unknown mutual coupling between elements, etc.), these traditional beamforming techniques are not viable, so a beamforming approach with relaxed requirements (blind beamforming) is required. This paper proposes a novel blind beamforming approach to address complex narrowband interference in spectrally congested environments where the precise array configuration is unknown. The resulting process is shown to suppress numerous interference sources, all without any knowledge of the primary signal of interest. The results are validated through wireless laboratory experimentation conducted with a two-element array, verifying that the proposed beamforming approach achieves a similar performance to the theoretical performance bound of receiving packets in additive white Gaussian noise (AWGN) with no interference present. Full article

This work introduces a high-performance multi-input multi-output (MIMO) antenna design to operate at the 28 GHz band. The proposed four-port MIMO antenna, in which each port comprises a 1 × 8 series-fed array, achieves peak gains of 13 dBi along with bandwidths of 1 GHz. Enhanced antenna performance is achieved through the optimal spacing of antenna elements and a decoupling methodology comprising a well-designed metamaterial unit cell, leading to reduced interference between antenna arrays. The design shows significantly suppressed mutual coupling to be less than −40 dB, a diversity gain that is very close to 10 dB, an envelope correlation coefficient of 0.00012, and a channel capacity loss of 0.147 bit/s/Hz, at 28 GHz. The experimental assessments confirmed these developments, endorsing the suggested design as a robust contender for 5G mmWave communications. Full article

A frequency-reconfigurable MIMO antenna with high gain, low mutual coupling and highly suppressed side lobe level (SLL) for applications in 24 GHz ISM band sensing and automotive radar systems was designed, modeled, and simulated. The reconfigurability feature was modeled with the implementation of a varactor diode in the model to alter the frequency in a wide band around 24 GHz. The design features 2- and 4-port MIMO antenna each comprising a 1 × 8 microstrip patch array. At the core of achieving both a high gain of 16 dBi and high isolation of 38.4 dB at a resonance frequency of 24.120 GHz lies the integration of a metamaterial absorber, comprising an optimized split-ring unit cell to effectively mitigate interference among the MIMO elements. Noteworthy impedance bandwidths of the sensor antenna span from 23.8 to 24.3 GHz, catering to diverse frequency requirements. The proposed sensor antenna feature a half-power beamwidth of 74° in the E-plane and 11° in the H-plane and an SLL of −24 dB at 24.120 GHz showing its robust performance characteristics across multiple operational dimensions. Full article

Ground-Penetrating Radar (GPR) is a non-destructive sensing technology that utilizes high electromagnetic frequencies. However, mutual interference waves caused by multiple scattering between targets can significantly complicate the interpretation of GPR B-scan images, especially when shallow targets obscure deeper ones. Existing methods primarily focus on extracting target signals from background clutter, frequently overlooking the impact of mutual interference. This paper proposes a convolutional neural network, termed MIS-SE-Net (Mutual Interference Suppression and Signal Enhancement Network), designed to suppress mutual interference waves while preserving shallow target signals and enhancing deeper ones. MIS-SE-Net incorporates attention gates into its encoder–decoder architecture, thereby improving its capabilities in interference suppression and enhancement of weak signals. The network is optimized using a combination of Mean Absolute Error (MAE) loss and perceptual loss. To evaluate MIS-SE-Net, the multi-scale weighted back projection (MWBP) imaging algorithm is used. Simulation results show that after processing with MIS-SE-Net, the integrated side-lobe ratio (ISLR) metric of MWBP imaging decreases by an average of 2.37%, while the signal-to-clutter ratio (SCR) increases by an average of 1.65%. For measured data, results show an average decrease of 7.51% in ISLR and an increase of 2.47% in SCR. These findings validate the effectiveness of the proposed method in suppressing interference, enhancing weak signals, and improving imaging quality. Full article

Compared with high spectrum efficiency faster-than-Nyquist (FTN) backbone network, an enhanced carrier phase recovery based on dual pilot tones is more sensitive to capital cost in FTN metropolitan areas as well as inter-datacenter optical networks. The use of distributed feedback (DFB) lasers is a way to effectively reduce the cost. However, under high symbol rate FTN systems, equalization-enhanced phase noise (EEPN) induced by a DFB laser with large linewidth will significantly deteriorate the system performance. What is worse, in FTN systems, tight filtering introduces inter-symbol interference so severe that the carrier phase estimation (CPE) algorithm of the FTN systems is more sensitive to EEPN, thus it will lead to a more serious cycle slip problem. In this paper, an enhanced carrier phase recovery based on dual pilot tones is proposed to mitigate EEPN and suppress cycle slip, in which the chromatic dispersion (CD)-aware Tx and LO laser phase noise is estimated, respectively. Offline experiments results under 40 Gbaud polarization multiplexing (PM) 16-quadrature amplitude modulation (QAM) FTN wavelength division multiplexing (FTN-WDM) systems at 0.9 acceleration factor, 5 MHz laser linewidth, and 500 km transmission demonstrate that the proposed algorithm could bring about 0.65 dB improvement of the required SNR for the normalized generalized mutual information of 0.9 compared with the training sequence-based cycle slip suppression carrier phase estimation (TS-CSS) algorithm. Full article

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function’s generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization. Full article

The visual measurement of deep holes in composite material workpieces constitutes a critical step in the robotic assembly of aerospace components. The positioning accuracy of assembly holes significantly impacts the assembly quality of components. However, the complex texture of the composite material surface and mutual interference between the imaging of the inlet and outlet edges of deep holes significantly challenge hole detection. A visual measurement method for deep holes in composite materials based on the radial penalty Laplacian operator is proposed to address the issues by suppressing visual noise and enhancing the features of hole edges. Coupled with a novel inflection-point-removal algorithm, this approach enables the accurate detection of holes with a diameter of 10 mm and a depth of 50 mm in composite material components, achieving a measurement precision of 0.03 mm. Full article

The simultaneous transmit and receive (STAR) array system provides higher radiation gain and data rate compared to traditional radio system. Because of the various mutual couplings between each pair of transmit and receive elements, it is a great challenge to suppress the incident self-interference power at multiple receive elements, which is usually much higher than the desired signal of interest (SoI) power and causes the saturation of receive links and the distortion of the digital SoI. In this paper, we propose an optimized method for transmit beamforming based on radiation power constraints and transmit power control. Through adaptive transmit beamforming, high isolation between the transmit array and each receive link is achieved, minimizing the self-interference power at each receiving element. This method effectively reduces the self-interference power, avoiding distortion of the SoI digital signal caused by limited-bit analog-to-digital converters (ADCs). Simulation results demonstrate that this optimized transmit beamforming method can achieve more than 100 dB effective isotropic isolation (EII) on a 32-element two-dimensional phased array designed in HFSS, reducing the maximum incident self-interference power at the receive channels by approximately 35 dB, while effectively controlling the attenuation of the transmit gain. We also present the advantages in receive subarray isolation and lower ADCs digits under the transmit ABF method. Full article

This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil exposure, soil straw residue, mutual shading of plant leaves, and light conditions. To address this issue, the YOLOv5s network model is improved by replacing the backbone network with the improved MobileNetv3, establishing a combination network model YOLOv5-M3 and using the convolutional block attention module (CBAM) to enhance detection accuracy. Distance-IoU Non-Maximum Suppression (DIoU-NMS) is used to improve the identification degree of the occluded targets, and knowledge distillation is used to increase the recall rate and accuracy of the model. The improved YOLOv5s target detection model is applied to the recognition and positioning of maize seedlings, and the optimal target position for weeding is obtained by max-min optimization. Experimental results show that the YOLOv5-M3 network model achieves 92.2% mean average precision (mAP) for crop targets and the recognition speed is 39 frames per second (FPS). This method has the advantages of high detection accuracy, fast speed, and is light weight and has strong adaptability and anti-interference ability. It determines the relative position of maize seedlings and the weeding machine in real time, avoiding squeezing or damaging the seedlings. Full article

Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper. Firstly, by differential processing of the raw millimeter-wave FMCW radar data, combined with threshold detection, the interfered sample area is located. Adaptive amplitude limiting is applied to the interfered samples to achieve initial suppression of the interference. Then, based on the VMD algorithm, the processed data are adaptively decomposed to obtain multiple intrinsic mode functions (IMFs). The Pearson correlation coefficient between each IMF and the signal before decomposition is calculated, and the IMF with the maximum Pearson correlation coefficient is extracted as the signal component to achieve the separation of the target signal from the interference and noise. The proposed method was validated based on simulation and experimental data. The results show that the proposed method achieves the best performance in terms of signal-to-interference-plus-noise ratio (SINR), mean square error (MSE), and kurtosis in frequency (KF) compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD). Further comparison was made with two typical methods, and the Range–Doppler (RD) map and SINR results showed that the proposed method exhibited certain performance advantages. Full article

The discriminative correlation filter (DCF)-based tracking method has shown good accuracy and efficiency in visual tracking. However, the periodic assumption of sample space causes unwanted boundary effects, restricting the tracker’s ability to distinguish between the target and background. Additionally, in the real tracking environment, interference factors such as occlusion, background clutter, and illumination changes cause response aberration and, thus, tracking failure. To address these issues, this work proposed a novel tracking method named the background-suppressed dual-regression correlation filter (BSDCF) for visual tracking. First, we utilize the background-suppressed function to crop out the target features from the global features. In the training step, while introducing the spatial regularity constraint and background response suppression regularization, we construct a dual regression structure to train the target and global filters separately. The aim is to exploit the difference between the output response maps for mutual constraint to highlight the target and suppress the background interference. Furthermore, in the detection step, the global response can be enhanced by a weighted fusion of the target response to further improve the tracking performance in complex scenes. Finally, extensive experiments are conducted on three public benchmarks (including OTB100, TC128, and UAVDT), and the experimental results indicate that the proposed BSDCF tracker achieves tracking performance comparable to many state-of-the-art (SOTA) trackers in a variety of complex situations. Full article

ECG signal processing is an important basis for the prevention and diagnosis of cardiovascular diseases; however, the signal is susceptible to noise interference mixed with equipment, environmental influences, and transmission processes. In this paper, an efficient denoising method based on the variational modal decomposition (VMD) algorithm combined with and optimized by the sparrow search algorithm (SSA) and singular value decomposition (SVD) algorithm, named VMD–SSA–SVD, is proposed for the first time and applied to the noise reduction of ECG signals. SSA is used to find the optimal combination of parameters of VMD [$K$,$\alpha$], VMD–SSA decomposes the signal to obtain finite modal components, and the components containing baseline drift are eliminated by the mean value criterion. Then, the effective modalities are obtained in the remaining components using the mutual relation number method, and each effective modal is processed by SVD noise reduction and reconstructed separately to finally obtain a clean ECG signal. In order to verify the effectiveness, the methods proposed are compared and analyzed with wavelet packet decomposition, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. The results show that the noise reduction effect of the VMD–SSA–SVD algorithm proposed is the most significant, and that it can suppress the noise and remove the baseline drift interference at the same time, and effectively retain the morphological characteristics of the ECG signals. Full article