IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Phase Measurement Deflectometry With Concave Mirror-Assisted Clear Imaging","authors":"Ziyu Li;Nan Gao;Zhaozong Meng;Zonghua Zhang;Yongjian Li","doi":"10.1109/TIM.2025.3564022","DOIUrl":"https://doi.org/10.1109/TIM.2025.3564022","url":null,"abstract":"Phase measuring deflectometry (PMD) is usually used to measure the specular surface accurately. However, due to the limited depth of field (DOF) of the camera lens, the traditional PMD cannot image the specular surface under test (SUT) and the display clearly together. As a result, the pixel position extraction and linear tracking of the display during the system calibration process and the process of the gradient solution are fuzzy and the measurement stability is reduced. According to the principle of concave mirror paraxial reflection imaging, this article introduces a concave mirror into the PMD and proposes a new PMD based on concave mirror (CM-PMD)-assisted imaging. The system model, calibration, and reconstruction methods are studied. The accuracy and reliability of the proposed CM-PMD method are verified by quantitative comparison experiments with the traditional PMD method. The experimental results demonstrate that the proposed CM-PMD method can effectively reduce the image blur caused by the inherent DOF of the camera and improve the precision and depth range of specular measurement.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Versatile Centrifuge-Coupled Lab-on-a-Compact Disk Hardware for Quantification of Cell Volume Fractions by Electrical Impedance Spectroscopy","authors":"Martin Wekesa Sifuna;Daisuke Kawashima;Prima Asmara Sejati;Masahiro Takei","doi":"10.1109/TIM.2025.3564018","DOIUrl":"https://doi.org/10.1109/TIM.2025.3564018","url":null,"abstract":"A versatile, centrifuge-coupled lab-on-a-compact disk (ccLoCD) hardware is developed for high through-put quantification of cell volume fractions (<inline-formula> <tex-math>$phi $ </tex-math></inline-formula>) by electrical impedance spectroscopy (EIS). The eight-electrode sensor hardware consists of 32-bit ARM microcontroller, impedance converter, and two high-speed analog multiplexer (MUX) modules on a disk mounted on a motor. It was evaluated by quantifying red blood cells (RBCs) of; 1. chicken (<inline-formula> <tex-math>$phi _{mathrm {chic}}^{mathrm {RBC}}$ </tex-math></inline-formula>) mixed with pork RBC with centrifugation and 2. pork RBC (<inline-formula> <tex-math>$phi _{mathrm {por}}^{mathrm {RBC}}$ </tex-math></inline-formula>) as they sediment into the sensor in saline (S), 50% plasma (50-P) and whole blood (WB) for assay of effects of protein concentration (<inline-formula> <tex-math>$text {Conc}^{mathrm {p}}$ </tex-math></inline-formula>) on erythrocyte sedimentation rates (ESRs). To check quantification accuracy (QA), <inline-formula> <tex-math>$phi _{mathrm {por}}^{mathrm {RBC}}$ </tex-math></inline-formula> (blood hematocrit) in cardiopulmonary bypass (CPB) was measured in comparison to <inline-formula> <tex-math>$phi _{mathrm {por}}^{mathrm {RBC}}$ </tex-math></inline-formula> from standard microcentrifuge (MC) method at increasing plasma volumes (<inline-formula> <tex-math>$v_{mathrm {p}}$ </tex-math></inline-formula>). Blood impedance, measured between 10 and 100 kHz, was analyzed by distribution of relaxation times (DRTs) and regression modeling to quantify <inline-formula> <tex-math>$phi $ </tex-math></inline-formula> by correlating (<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>) amplitude maxima (<inline-formula> <tex-math>$gamma _{max }$ </tex-math></inline-formula>) to <inline-formula> <tex-math>$phi $ </tex-math></inline-formula>. From results at characteristic relaxation time <inline-formula> <tex-math>$tau _{mathrm {c}} = 1times 10^{-5}$ </tex-math></inline-formula> s, differences in <inline-formula> <tex-math>$gamma _{max }$ </tex-math></inline-formula> before and after centrifugation were statistically significant (<inline-formula> <tex-math>$p lt 0.05$ </tex-math></inline-formula>) with centrifugation increasing QA thus raising <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> from 0.0242 to 0.989. In 15 min, differences in <inline-formula> <tex-math>$phi _{mathrm {por}}^{mathrm {RBC}}$ </tex-math></inline-formula> for RBC sedimentation in S, 50%-P, and WB were also statistically significant (<inline-formula> <tex-math>$p lt 0.05$ </tex-math></inline-formula>) showing capacity of hardware to capture <inline-formula> <tex-math>$text {Conc}^{mathrm {p}}$ </tex-math></inline-formula> effects. In QA analysis, hardware-based <inline-formula> <tex-math>$phi _{mathrm {por}}^{mathrm {RBC}}$ </tex-math></inline-formula> had low 0.73% error relative to MC-based <inline-formula> <tex-math>$phi","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Induction-Based Planar Displacement Sensing With Sub-Micrometer Resolution","authors":"Abhilash Patel;V. Kartik","doi":"10.1109/TIM.2025.3562982","DOIUrl":"https://doi.org/10.1109/TIM.2025.3562982","url":null,"abstract":"Displacement sensing at the sub-micrometer scale is crucial for applications such as precision manufacturing, micropositioning, and scanning probe microscopy. In this article, a novel method is proposed that uses magnetic induction for measuring sub-micrometer displacements in three degrees of freedom. This approach uses simple coils and a synchronous demodulation circuit for measurement. An excitation coil generates an alternating magnetic field and four sensing coils measure its instantaneous intensity. Fit functions are proposed to estimate the position from each sensing coil’s output; these functions can calculate displacement over a large range with small errors. The sensor’s output characteristics are found to be highly dependent upon the shape of the coils. An analytical approach is presented to predict the output characteristics for various shapes of the sensing and excitation coils, and is used to test the sensitivity for various shape combinations. The developed sensor is characterized and a (<inline-formula> <tex-math>${pm } sigma $ </tex-math></inline-formula>) resolution of 600 nm is achieved over a bandwidth of 100 Hz and a range of <inline-formula> <tex-math>$200~mu $ </tex-math></inline-formula>m. Further, this displacement signal is fed back to a piezo stage to eliminate positioning errors that arise due to hysteresis. A 70% (rms) reduction in tracking error is achieved in closed-loop operation, relative to the open-loop. Synchronous detection reduces power supply noise levels by 24 dB, improving the signal-to-noise ratio. The method is immune to electromagnetic interference (EMI), stray magnetic fields, and environmental ingress, making it a suitable option for use in high-noise industrial applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lithium-Ion Battery RUL Prediction Method Based on ConvTrans and tAPE Under Capacity Regeneration Noise and Low-Dimensional Time Series Data","authors":"Jiayu Chen;Qinhua Lu;Xuhang Wang;Hongjuan Ge;Min Xie","doi":"10.1109/TIM.2025.3564016","DOIUrl":"https://doi.org/10.1109/TIM.2025.3564016","url":null,"abstract":"Remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) is essential for ensuring their optimum performance and longevity in energy storage solutions. However, it is difficult to predict RUL accurately under the capacity regeneration noise, especially with low-dimensional long time series data. Therefore, a novel RUL prediction method of LIB is proposed based on convolutional Transformer (ConvTrans) combined with time absolute position encoding (tAPE). First, complete ensemble empirical mode decomposition with adaptive noise is introduced to decompose the capacity degradation sequence into several components. Then, to accurately assess the importance of each component in reconstructing the original signal, random forest (RF) regression and Gini index are applied to obtain the weights of each component, which measures its ability to interpret the original sequence. Next, a ConvTrans is proposed to capture both short-term and long-term dependencies in battery data, which can depict the overall degradation trend of the battery without missing important local details. Moreover, combined with tAPE, which fits for processing low-dimensional long time series data, the improved ConvTrans can accurately model the battery degradation process and evaluate the RUL. Finally, comprehensive cases have been studied, and the results validate the effectiveness and superiority of the proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Reconstruction Method for Weak Magnetic Pipeline Inspection Signals Based on Adaptive Multiscale Signal Reconstruction","authors":"Bin Liu;Yuze Liu;Zheng Lian;Zihan Wu;Luyao He;Lijian Yang","doi":"10.1109/TIM.2025.3563915","DOIUrl":"https://doi.org/10.1109/TIM.2025.3563915","url":null,"abstract":"The weak magnetic pipeline stress detection technology is at the forefront of international pipeline safety maintenance. However, the noise embedded in weak magnetic stress detection signals significantly impacts detection accuracy. To address this issue, this article proposes a reconstruction method for weak magnetic inspection data that integrate adaptive parameter optimization and multiscale signal decomposition (MSD), aiming to enhance the detection accuracy of weak magnetic stress inspection systems. First, the method combines intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) with MSD technology to handle residual complex noise. Second, fuzzy entropy (FE) is employed for the selection of intrinsic mode functions (IMFs) to tackle the nonstationarity of weak magnetic signals. Finally, an improved sparrow search algorithm (ISSA) is introduced to dynamically adjust key parameter configurations, effectively resolving tuning difficulties and significantly improving signal reconstruction performance. The method’s performance is evaluated through the reconstruction of synthetic signals and weak magnetic simulation signals of external pipeline defects, as well as reconstruction experiments on weak excitation signals collected from Q235 pipelines. The results indicate that the method, while ensuring the highest signal-to-noise ratio (SNR), significantly reduces the total variation (TV) from 20887.5 to 1158.6 and reduces the peak distortion (Pd) factor to approximately 57, improving by about 27.62% compared to the second-best method. Furthermore, the inversion of pipeline stress distribution demonstrates that this method can effectively improve model accuracy and robustness.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WLTCL: Wide-Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System","authors":"Guodong Sun;Mingjing Li;Dingjie Liu;Mingxuan Liu;Bo Wu;Yang Zhang","doi":"10.1109/TIM.2025.3564017","DOIUrl":"https://doi.org/10.1109/TIM.2025.3564017","url":null,"abstract":"As an essential component of logistics automation, the automated loading system is becoming a critical technology for enhancing operational efficiency and safety. Precise automatic positioning of the truck compartment, which serves as the loading area, is the primary step in automated loading. However, existing methods have difficulty adapting to truck compartments of various sizes, do not establish a unified coordinate system for light detection and ranging (LiDAR) and mobile manipulators, and often exhibit reliability issues in cluttered environments. To address these limitations, this study focuses on achieving precise automatic positioning of key points in large, medium, and small fence-style truck compartments in cluttered scenarios. We propose an innovative wide-field-of-view (FOV) 3-D LiDAR vehicle compartment automatic localization system. For vehicles of various sizes, this system leverages the LiDAR to generate high-density point clouds within an extensive FOV range. By incorporating parking area constraints, our vehicle point cloud segmentation method more effectively segments vehicle point clouds within the scene. Our compartment key point positioning algorithm utilizes the geometric features of the compartments to accurately locate the corner points, providing stackable spatial regions. Extensive experiments on our collected data and public datasets demonstrate that this system offers reliable positioning accuracy and reduced computational resource consumption, leading to its application and promotion in relevant fields.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Tea Leaf Blight From UAV Remote Sensing Images in Overcast and Low-Light Conditions","authors":"Gensheng Hu;Jiahang Zou","doi":"10.1109/TIM.2025.3564020","DOIUrl":"https://doi.org/10.1109/TIM.2025.3564020","url":null,"abstract":"Detecting tea leaf blight (TLB) in unmanned aerial vehicle (UAV) remote sensing images is a challenging task. Under overcast and low-light conditions, existing detection methods face problems such as the small size of the TLB spots, their similarity to the backgrounds, and poor clarity, which results in low detection accuracy. To solve these problems, this study proposes a method based on a combination of an image enhancement network and an efficient object detection network. First, the image is feature-enhanced by the detection task-driven data enhancement network, called UAV detection enhancement network (UDENet), to reduce the influence of insufficient light under overcast weather condition and retain the discriminative information conducive to TLB detection. Second, TLB is detected in the enhanced UAV images by the TLB detection network UTDet. UTDet performs feature extraction on the feature-enhanced UAV remote sensing images through space-frequency multiaxis subnetwork (SFMASNet), which effectively improves the feature extraction capability for small objects in UAV remote sensing images. SFMASNet also avoids the influence of complex backgrounds on the detection results and improves the detection accuracy through the use of an aggregated contextual attention subnetwork (ACASNet). The experimental results show that compared with the existing detection methods, the proposed UDENet combined with UTDet gives the best accuracy for the detection of TLB in UAV remote sensing images under overcast and low-light conditions, with values for the precision (P), recall (R), and mean average precision (mAP) of 67.4%, 65.0%, and 68.6%, respectively. The proposed detection method achieves an mAP value that is 4.7% higher than the baseline network and more than 3.6% higher than the state-of-the-art detection methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-19"},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRDepth: Pose Refinement Enhancement-Based Monocular Depth Estimation for Indoor Scenes","authors":"Chenggong Han;Chen Lv;Xiaolin Huang;Qiqi Kou;Deqiang Cheng;He Jiang","doi":"10.1109/TIM.2025.3562976","DOIUrl":"https://doi.org/10.1109/TIM.2025.3562976","url":null,"abstract":"Indoor depth measurement is widely used in technologies such as virtual reality and augmented reality. However, indoor scenes are typically captured with handheld cameras, resulting in more complex and unpredictable variations between frames. Self-supervised depth estimation relies on frame-to-frame projection for self-constraint, and inaccurate pose predictions between frames significantly hinder depth estimation in indoor environments. To address this issue, PRDepth is proposed, a self-supervised pose refinement method tailored for indoor environments. PRDepth introduces a pose reconstruction iterative module (PRIM) that refines multiframe pose decomposition and reconstruction. By leveraging contextual information from intermediate frames, it mitigates estimation errors caused by large rotations and reduces training errors due to inaccurate rotations, leading to more precise pose predictions between frames. Additionally, to enhance the information exchange and context integration capabilities of the depth network, PRDepth features a depth-weighted incentive module, which includes a global depth enhancement module (GDEM) in the encoder-decoder and a weight-adaptive incentive module (WAIM) in the decoder. The GDEM improves the network’s ability to extract depth information in complex scenes by interacting with global cross-dimensional data. An attention-guided mechanism is adopted by the WAIM to aggregate multiscale feature information and assign adaptive weights to different features, ensuring efficient global context fusion and suppression of redundant information. Experimental results demonstrate that our method significantly outperforms existing state-of-the-art self-supervised monocular depth estimation techniques for indoor scenes. Extensive ablation studies are conducted on each module of PRDepth. PRDepth demonstrates precise depth estimation and robust generalization across indoor datasets, including NYUv2, 7-Scenes, ScanNet, and InteriorNet.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A LiDAR–Camera Spatiotemporal Synchronization Method for Unmanned Aerial Vehicle-Based Ground Target Perception","authors":"Keren Dai;Jie Zhu;Haiting Hou;Qingyu Li;Xiang Ma;Hang Yu","doi":"10.1109/TIM.2025.3558243","DOIUrl":"https://doi.org/10.1109/TIM.2025.3558243","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been widely adopted in military reconnaissance, urban management, and agricultural monitoring. However, the increasing complexity of real-world environments, characterized by dynamic targets and variable ambient conditions, presents significant challenges for multisensor data fusion. While integrating heterogeneous sensors such as light detection and ranging (LiDAR) and cameras enhances ground target perception, differences in sampling rates and sensor characteristics often lead to spatial and temporal misalignment. Traditional synchronization methods, including static extrinsic calibration and hardware-based triggers such as inertial measurement units (IMUs) or global navigation satellite system (GNSS), struggle to adapt to rapidly changing scenarios and complex backgrounds. To address these challenges, we propose a novel and cost-effective spatiotemporal synchronization method that leverages prominent ground features—such as terrain contours and building outlines—as reference benchmarks, combined with a timestamp-driven pulse width modulation (TSD-PWM) sampling technique. This approach eliminates the need for additional synchronization hardware while ensuring adaptability across diverse environments, ranging from dense urban landscapes with intricate architectural structures to rural areas with natural terrain variations. Extensive real-world experiments demonstrate that our method improves time synchronization accuracy by 82.65% over interpolation-based techniques and enhances spatial synchronization accuracy by 74.23% compared to reprojection methods based on static extrinsic parameters. These results highlight the effectiveness of our approach in mitigating spatiotemporal asynchrony in UAV-based ground target perception using heterogeneous sensors.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Self-Test Method for MEMS Gyroscope With Equivalent Inertial Force","authors":"Rui Zhou;Rang Cui;Chong Shen;Yunbo Shi;Jingfeng Yu;Yanchao Ren;Huiliang Cao","doi":"10.1109/TIM.2025.3561371","DOIUrl":"https://doi.org/10.1109/TIM.2025.3561371","url":null,"abstract":"This article introduces a self-test approach for micro-electromechanical system (MEMS) vibrating gyroscopes based on the concept of equivalent inertial force. By applying electrostatic signals to the feedback electrode, it is possible to simulate the inertial force input, enabling measurement of the gyroscope scale factor (SF) without reliance on physical testing equipment. This article presents the operational principle of equivalent inertial force, establishes the correlation between equivalent inertial force and angular velocity through derivation, and accomplishes the calibration of this relationship across a broad temperature range using quadrature feedback forces. In contrast to traditional methods, this approach facilitates direct measurement of the SF of the gyroscope and compensates for variations in the SF induced by alterations in the temperature and environmental conditions. The efficacy of the proposed approach was validated in a comparative experiment on a ring resonator gyroscope. In the open-loop mode, the average calibration error of the SF of the equivalent inertial force method and the turntable method is 5.608%. The long-term self-test repeatability is 1781.2 ppm. Furthermore, the SF self-compensation of the gyroscope within the temperature range of <inline-formula> <tex-math>$- 20~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$+ 40~^{circ }$ </tex-math></inline-formula>C was achieved using the self-test results. The temperature sensitivity of the SF before and after compensation changed from 6689.7 ppm/°C to 318.6 ppm/°C, which was reduced by <inline-formula> <tex-math>$20times $ </tex-math></inline-formula>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}