Measurement最新文献

Airborne synthetic aperture radar (SAR) has the advantages of flexible scanning geometry and high spatial resolution compared with spaceborne SAR. However, the quality and positioning accuracy are affected by platform instability caused by air turbulence and other factors. In this paper, the sources of geometric positioning error in airborne SAR were analyzed, including platform position error, time delay error and tropospheric delay error. To improve the positioning quality of airborne SAR, an imaging parameters calibration model considering velocity constraints (IFP-VC) was proposed to rectify the orbit parameters and enable geometric calibration to improve the positioning accuracy. The proposed IFP-VC model was applied to improve the positioning accuracy of airborne SAR images acquired in Hainan and Rizhao, China. The IFP-VC model demonstrated good positioning performance when using measured ground control points (GCPs) in Hainan, achieving a positioning accuracy of 1.42 m. In Rizhao, the proposed IFP-VC model yielded a positioning accuracy of 7.16–12.71 m and 2.05–6 m higher than the slant range and time calibration model and the unconstrained model when using manually selected GCPs, respectively. The multiple images combination strategy was superior to the single image strategy, and the initial positioning accuracy of 930 m was improved from 14.50 m using a single image to 12.47 m using 5 images. When applying the imaging parameters calibration results to the images in different tracks, the positioning accuracy can be improved to 31 m compared with the initial positioning accuracy. The geometric calibration of airborne SAR images has the potential to enhance its applications in remote sensing mapping, land-use change monitoring and ground target detection.

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and $10^{-4}$ relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

With the miniaturization of quantum sensors, the fabrication of alkali metal vapor cells based on micro-electromechanical systems (MEMS) has become increasingly important. However, the fabrication of MEMS vapor cells remains a challenge. In this study, a microfabricated millimeter-level vapor cell was fabricated and tested. The silicon cavity was fabricated by dry etching and chemical polishing. After injection of alkali metal and buffer gas, a two-step low-temperature anodic bonding was employed. The first step was non-isothermal to keep the alkali metal in the low-temperature pole plate and the second step was isothermal to improve the bonding strength, enhancing the hermeticity. Then, the vapor cell was tested for sidewall roughness, bonding strength, and leakage rate. Subsequently, Rb D1 line absorption spectroscopy, stability, and the single-beam magnetometer signal were tested. The results show the fabricated vapor cell had high stability, providing a basis for the miniaturization of quantum devices.

Due to unpredictable environmental factors, the measurement noise in INS/GNSS integration can be affected by outliers or exhibit statistical uncertainty. A single adaptive or robust filter may not be suitable for all noise scenarios. To address this issue, a new approach called the measurement noise covariance matrix (MNCM) ’R’ modified centered error entropy cubature Kalman filter (RMCEECKF) is proposed in this study. In this method, the MNCM is adjusted based on the innovation sequence, and outliers are detected using the Mahalanobis distance. If outliers are identified, the CEE criterion with strong robustness is applied for the posterior update. Simulation results on INS/GNSS integration demonstrate that the RMCEECKF offers higher estimation accuracy compared to existing methods in scenarios involving outliers, uncertain noise covariance, and outliers under uncertain noise covariance. The inclusion of outlier detection also enhances the computational efficiency of RMCEECKF when compared to the CEECKF.

The incorporation of Artificial Intelligence (AI) is pivotal in automating intricate technical tasks, significantly enhancing accuracy and efficiency while alleviating the burdens of repetitive monitoring traditionally borne by technicians. This study focuses on developing a customized four-probe station integrated with sophisticated AI models aimed at classifying current–voltage ($I-V$) characteristics and extracting essential parameters. Our methodology encompasses the fabrication of precision-engineered gold-plated probes, meticulously assembled with a three-dimensional (3D) moving head to ensure optimal contact and measurement fidelity across a variety of electronic and optoelectronic devices. Data acquisition is executed via a source meter unit, followed by rigorous post-processing utilizing advanced algorithms, including convolutional neural networks and random forest techniques. Notably, the gold-plated contacts enhance measurement accuracy by providing superior conductivity and minimizing contact resistance, while the movable head allows for dynamic adjustment, facilitating precise probe alignment for consistent data retrieval. The results demonstrate a remarkable capability in classifying $I-V$ characteristics with a root-mean-square (RMS) error of less than 1%, underscoring the system’s reliability and accuracy. Moreover, our predictive models effectively utilize previously recorded measurements to forecast the degradation profiles of devices, thus offering significant insights into device longevity and performance.

We report a novel speech recognition method using a noise-robust acoustic sensor system that integrates a spatially frequency-separating sensor with a nonlinear amplification algorithm, mimicking the cochlea’s basilar membrane and hair cells. The multichannel piezoelectric artificial basilar membrane (ABM) sensor detects specific sound frequencies with high sensitivity over 0.2–6 kHz. The signal processing model of the Artificial Hair Cell inspired by the signal transduction mechanism of inner hair cells, simultaneously enhances the frequency selectivity of ABM sensors and improves noise robustness. In a 0 dB SNR noisy environment, it effectively detected the voice signal with a maximum SNR of 57 dB. Furthermore, we converted the frequency-separated signals for speech sounds in various noisy environments into heatmap images and utilized them as input for a CNN-based speech recognition algorithm. Consequently, our system demonstrated noise-robust recognition performance with 94 % accuracy, even in noisy environments.

This paper presents a method for active detection and localization using a micro-sonar buoy equipped with a MEMS vector hydrophone. The approach enhances the signal-to-noise ratio and mitigates environmental noise by utilizing a high sound source level and an omnidirectional explosive sound source. By leveraging the vectorial properties of the MEMS vector hydrophone, a sound pressure–velocity processing algorithm and zero-padding Generalized Cross Correlation-Phase Transform (GCC-PHAT) technique were proposed. This method enables precise localization of the target by integrating target latitude and longitude calculations. The indoor experiments demonstrate that the azimuth angle error is less than 1°, and field tests show a distance error of 1.295 m between the buoy and the target. These results validate the effectiveness and accuracy of the proposed method. This research offers both theoretical insights and experimental validation, laying a foundation for future advancements and applications in the field.

This research focuses on developing a denoising filter that effectively enhances subtle non-stationarities within signals. Initially, the spectral kurtosis has been calculated from each frequency bin of the spectrogram (setting initial values) which is further optimized by the flow direction algorithm (FDA) to obtain a vector of optimized spectral kurtosis as the classical spectral kurtosis as a data-driven estimator may be not optimal in the sense of informative signal extraction for some specific signals with complex time–frequency structure. Due to this, the vectors obtained from optimized spectral kurtosis for a given frequency bin could be even zero if they do not support the impulsiveness of the output signal. In this way, it allows not to use any thresholding that is always complicated. The vector of the optimized spectral kurtosis thus obtained has been used to design the filter that can extract the impulsive components from the signal.

In this study, an in-situ suppression for the high-frequency magnetic field (HFMF) generated by the electric heater based on the detection of the atomic magnetometer signal is proposed. Specifically, by modeling the response of atomic spin precession signals from an optically pumped atomic magnetometer to the HFMF, the in-situ suppression is achieved by the radio frequency fields produced by coils to compensate the HFMF. The suppression of the HFMF generated by the electric heater is judged based on the amplitude response of the spin precession signal detected at the atomic resonance. Our proposed method is validated through both simulation and experimental results. Before and after in-situ suppression of the HFMF generated by the electric heater, the sensitivity of 13000 nT and 15000 nT static magnetic field within the heating frequency band is measured, resulting in approximately 50% and 60% increases in sensitivity after suppression, respectively.

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.