Search Results (95)

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L₁₆ (4⁴) orthogonal experimental design. The key parameters investigated include pulse width (T_on), pulse interval (T_off), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (R_a) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at T_on = 28 μs, T_off = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality. Full article

Lidar has been used for many years to study the optical properties of aerosols, but estimating the boundary values requires solving the lidar elastic scattering equation, which remains a challenge. The boundary values are often determined by fitting to uniform regions of the atmosphere. This method typically excludes low signal-to-noise ratio (SNR) signals because it classifies them as non-uniform, reducing the effective detection range of the lidar. On the other hand, directly fitting low SNR signals to estimate the boundary values can introduce significant errors. The method is based on maximizing the lidar detection distance and determines the boundary value using a new estimation algorithm with the averaging of multiple fitted results in the low SNR region to reduce the impact of noise. Simulations demonstrate that the new method reduces the relative error in the boundary value estimation by approximately 5% and improves the accuracy of the extinction coefficient profile inversion compared with the method of directly fitting all-sample signals. Field comparison experiments with forward-scattering sensors further verify that the algorithm improves the retrieval accuracy by 17.3% under extremely low signal-to-noise ratio (SNR) conditions, while performing comparably to the traditional method in high SNR homogeneous atmospheres. Additionally, based on the scanned lidar signals, the algorithm can provide detailed information on the spatial distribution of sea fog and offer valuable insights for an in-depth understanding of the physical evolution of sea fog. Full article

This paper proposes a novel method for Direction of Arrival (DOA) estimation using a deep unfolded LISTA network in a non-uniform metasurface. Traditional DOA estimation methods often face challenges such as limited accuracy, high computational complexity, and poor adaptability to complex signal environments. To address these issues, we optimize a non-uniform metasurface array to reduce hardware costs and mutual coupling effects while enhancing resolution. Additionally, a deep unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) network is constructed by transforming Iterative Shrinkage Thresholding Algorithm (ISTA) iterative steps into trainable neural network layers, combining model-driven logic with data-driven parameter optimization. Simulation results prove that this method enhances higher precision and reduces computational complexity in comparison with traditional algorithms, especially under low SNR conditions. Furthermore, the method exhibits greater generalization ability, making it a reliable approach for high-precision DOA estimation in practical applications. Full article

Non-destructive testing (NDT) plays a crucial role in ensuring the structural integrity and safety of industrial facilities and components. Long pulse thermography (LPT), a form of active thermographic testing (ATT), has gained attention for its ability to detect subsurface defects efficiently. However, non-uniform thermal excitation and environmental noise often degrade the accuracy of defect detection. This study proposes an advanced thermographic inspection technique incorporating a halogen array (HA) lamp and a compensation methodology to enhance the reliability of defect detection. Two compensation methods, namely absolute temperature compensation (ATC) and temperature rate compensation (TRC), were developed to correct non-uniform thermal loads and improve the defect contrast. Experimental validation was conducted on A-type and B-type mock-up specimens with artificial subsurface defects (10–90% depth). The results demonstrated a significant enhancement in the signal-to-noise ratio (SNR), reaching up to a 42 dB improvement in severe defects. Furthermore, a quantitative evaluation method was proposed using SNR-based defect depth estimation models, improving the accuracy of defect sizing. This approach eliminates the need for complex amplitude and phase transformations, enabling direct defect assessment from temperature thermograms. Full article

Following the success of Visible Infrared Imaging Radiometer Suite (VIIRS) instruments currently operating onboard the Suomi-NPP, NOAA-20, and NOAA-21 spacecraft, preparations are underway for the final two VIIRS instruments for the Joint Polar Satellite System 3 (JPSS-3) and 4 (JPSS-4) platforms. To that end, each instrument underwent a comprehensive sensor-level test campaign at the Raytheon Technologies, El Segundo facility, in both ambient and thermal-vacuum environments. Unique among the 22 VIIRS sensing bands is the day-night band (DNB)—a panchromatic imager that leverages multiple CCD detectors set at different gain levels to make continuous (day and night) radiometric observations of the Earth. The results from the JPSS-3 and JPSS-4 VIIRS DNB pre-launch testing are presented and compared against the design specifications in this paper. Characterization parameters include dark offset, gain, linearity, uniformity, SNR, and uncertainty. Performance relative to past builds is also included where appropriate. Full article

With the rapid development of hyperspectral applications using unmanned aerial vehicles (UAVs), the traditional assumption that ground objects exhibit Lambertian reflectance is no longer sufficient to meet the high-precision requirements for quantitative inversion and airborne hyperspectral data applications. Therefore, it is necessary to establish a hyperspectral bidirectional reflectance distribution function (BRDF) model suitable for the area of imaging. However, obtaining multi-angle information from UAV push-broom hyperspectral data is difficult. Achieving uniform push-broom imaging and flexibly acquiring multi-angle data is challenging due to spatial distortions, particularly under heightened roll or pitch angles, and the need for multiple flights; this extends acquisition time and exacerbates uneven illumination, introducing errors in BRDF model construction. To address these issues, we propose leveraging the advantages of multi-spectral cameras, such as their compact size, lightweight design, and high signal-to-noise ratio (SNR) to reconstruct hyperspectral multi-angle data. This approach enhances spectral resolution and the number of bands while mitigating spatial distortions and effectively captures the multi-angle characteristics of ground objects. In this study, we collected UAV hyperspectral multi-angle data, corresponding illumination information, and atmospheric parameter data, which can solve the problem of existing BRDF modeling not considering outdoor ambient illumination changes, as this limits modeling accuracy. Based on this dataset, we propose an improved Walthall model, considering illumination variation. Then, the radiance consistency of BRDF multi-angle data is effectively optimized, the error caused by illumination variation in BRDF modeling is reduced, and the accuracy of BRDF modeling is improved. In addition, we adopted Transformer for spectral reconstruction, increased the number of bands on the basis of spectral dimension enhancement, and conducted BRDF modeling based on the spectral reconstruction results. For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. In order to evaluate BRDF modeling and quantitative application potential from the reconstruction results, we conducted comparison and ablation experiments. Finally, we solved the problem of difficulty in obtaining multi-angle information due to the limitation of hyperspectral imaging equipment, and we provide a new solution for obtaining multi-angle features of objects with higher spectral resolution using low-cost imaging equipment. Full article

The quantization process of synthetic aperture radar (SAR) images faces significant challenges due to their high dynamic range, resulting in notable quantization distortion. This not only degrades the visual quality of the quantized images but also severely impacts the accuracy of image interpretation. To mitigate the distortion caused by uniform quantization and enhance visual quality, this paper introduced a novel nonlinear quantization framework via signal-to-noise ratio (SNR) enhancement and segmentation strategy guidance. This framework introduces guiding information to improve quantization performance in weak scattering regions. A histogram adjustment method is developed to incorporate the spatial information of SAR images into the quantization process to enhance the quantization performance, specifically within weak scattering regions. Additionally, the optimal quantizer is improved by refining the SNR distribution across quantization units, addressing imbalances in their allocation. Experimental results based on Gaofen-3 (GF-3) satellite data demonstrate that the proposed algorithm approaches the global quantization performance of optimal quantizers while achieving superior local quantization performance compared to existing methods. Full article

With the development of technology, MEMS microphones, which are small-sized and highly uniform, have been applied extensively. To improve their reliability in extreme environment and overcome the constraints of traditional microphones, this article presents a piezoelectric bimorph MEMS microphone using ${\mathrm{Al}}_{0.8}{\mathrm{Sc}}_{0.2}\mathrm{N}$. In the article, the high robustness of piezoelectric microphones and the reasons for choosing ${\mathrm{Al}}_{0.8}{\mathrm{Sc}}_{0.2}\mathrm{N}$ as piezoelectric materials are described. The sensitivity of an ${\mathrm{Al}}_{0.8}{\mathrm{Sc}}_{0.2}\mathrm{N}$-based piezoelectric bimorph compared with the traditional structure are revealed through FEA. Subsequently, a lumped element microphone model is constructed and all noise sources are evaluated comprehensively. The difference in output noise caused by different structures is calculated. The designed piezoelectric microphone, which comprises eight triangular cantilever beams, was fabricated on a chip with an area of 900 $\mu$m × 900 $\mathrm{\mu }$m. The sensitivity of the designed microphone achieves 1.68 mV/Pa, with a noise floor of −110 dBA and SNR of 54.5 dB. The acoustic overload point of the microphone stands at 147 dB SPL, and following the impact test, the survival rate was 100%. Compared to traditional MEMS microphones, the microphone achieves a dynamic range of 107.5 dB. Full article

In the field of direction of arrival (DOA) estimation, a common assumption is that array noise follows a uniform Gaussian white noise model. However, practical systems often encounter non-ideal noise conditions, such as non-uniform or colored noise, due to sensor characteristics and external environmental factors. Traditional DOA estimation techniques experience significant performance degradation in the presence of colored noise, necessitating the exploration of specialized DOA estimation methods for such environments. This study introduces a DOA estimation method for acoustic vector arrays based on a spectrum attention mechanism (SAM). By employing SAM as an adaptive filter and constructing a double-branch model combining a convolutional neural network (CNN) and long short-term memory (LSTM), the method extracts the spatial and temporal features of signals, and effectively reduces the frequency components of colored noise, enhancing DOA estimation accuracy in colored noise scenarios. At an SNR of −5 dB, it achieves an accuracy rate of 85% while maintaining a low RMSE of only 2.03°. Full article

Due to their compact sizes, low power consumption levels, and convenient integration capabilities, piezoelectric micromachined ultrasonic transducers (PMUTs) have gained significant attention for enabling environmental sensing functionalities. However, the frequency inconsistency of the PMUT arrays often leads to directional errors with the ultrasonic beams. Herein, we propose a reconfigurable PMUT array based on a Sc_0.2Al_0.8N piezoelectric thin film for in-air ranging. Each element of the reconfigurable PMUT array possesses the ability to be independently replaced, enabling matching of the required frequency characteristics, which enhances the reusability of the device. The experimental results show that the frequency uniformity of the 2 × 2 PMUT array reaches 0.38% and the half-power beam width (θ_−3dB) of the array measured at 20 cm is 60°. At a resonance of 69.7 kHz, the sound pressure output reaches 7.4 Pa (sound pressure level of 108.2 dB) at 19 mm, with a reception sensitivity of approximately 11.6 mV/Pa. Ultimately, the maximum sensing distance of the array is 7.9 m, and it extends to 14.1 m with a horn, with a signal-to-noise ratio (SNR) of 19.5 dB. This research significantly expands the ranging capability of PMUTs and showcases their great potential in environmental perception applications. Full article

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system’s ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust. Full article

In this paper, a novel method is introduced to enhance the performance of vehicle-to-vehicle (V2V) visible light communication (VLC) by employing different transmitter (Tx) light-emitting diode (LED) array arrangements with different LED orientations. Improving the signal-to-noise ratio (SNR) is crucial for V2V VLC systems to provide long communication ranges. For this purpose, six transmitter configurations are proposed: single-LED transmitters, as well as 3 × 3 square-, single hexagonal-, octagonal-, 5 × 5 square-, and honeycomb hexagonal-shaped LED arrays. Indoor VLC studies using LED arrays offer a uniform SNR, while outdoor studies focus on optimizing the receiver side to enhance system performance. This paper optimizes system performance by increasing the SNR and communication range of V2V VLC systems by changing the geometry of the Tx LED array and LED orientations. A V2V VLC system using on–off keying (OOK) is modeled in MATLAB, and the SNR and bit error rate (BER) are simulated for different Tx configurations. Our results show that the honeycomb hexagonal transmitter design provides a 19% improvement in system performance with a spacing of 1 cm, and maintains a 16% improvement when the array size is reduced by a factor of 100, making it smaller than one of the smallest industrial headlight modules. Full article