Precise Calibration Research Articles

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Abstract The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, 3D defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex (FMR) algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.

Micromachined needle-like calorimetric flow sensor for sap flux density measurement in the xylem of plants

Miniaturized silicon thermal probes for plant’s sap flow measurement, or micro sap flow sensors, have advantages in minimum invasiveness, low power consumption, and fast responses. Practical applications in sap flow measurement has been demonstrated with the single-probe silicon micro sensors. However, the sensors could not detect flow directions and require estimating zero sap flow output that leads to significant source of uncertainty. Furthermore, silicon-needles would break easily during the insertion into plants. We present the first three-element micro thermal sap flow sensor packaged on a durable printed circuit board needle that can measure bidirectional flows with improved dynamics and precision. The performance of the newly designed calorimetric flow sensor was confirmed through precision calibration and field test on tomato stems. A calibration curve for a tomato stem was obtained with a sensitivity of 0.299 K/(µL mm−2 s−1) under the maximum temperature increase of 4.61 K. Results from the field test for one month revealed a correlation between the measured sap flux density and related conditions such as solar radiation, vapor pressure deficit, sunshade and irrigation. The developed sensor will contribute to practical long-term sap flow monitoring for small and delicate plants with minimal physical invasion.

Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression

ObjectivesTo explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP).MethodsA total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model 2: plaque characteristics model. Model 3: PCAT radiomics model. Model 4: clinical + radiomics model. Model 5: plaque characteristics + radiomics model. The evaluation of the models encompassed identification accuracy, calibration precision, and clinical applicability. Delong’ test was employed to compare the area under the curve (AUC) of different models.ResultsSeven radiomic features, including two shape features, three first-order features, and two textural features, were selected to build the PCAT radiomics model. In contrast to the clinical model and plaque characteristics model, the PCAT radiomics model (AUC 0.85 for training, 0.84 for internal validation, and 0.81 for external validation; p < 0.05) achieved significantly higher diagnostic performance in predicting RPP. The separate combination of radiomics with clinical and plaque characteristics model did not further improve diagnostic efficacy statistically (p > 0.05).ConclusionRadiomic feature analysis derived from PCAT significantly improves the prediction of RPP as compared to clinical and plaque characteristics. Radiomic analysis of PCAT may improve monitoring RPP over time.Critical relevance statementOur findings demonstrate PCAT radiomics model exhibited good performance in the prediction of RPP, with potential clinical value.Key PointsRapid plaque progression may be predictable with radiomics from pericoronary adipose tissue.Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression.Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression.Graphical

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.

Probability-Based LIDAR-Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map.

The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR-camera's external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods.

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Determining ideal offsets of spatially offset Raman spectroscopy for transcutaneous measurements-A Monte Carlo study.

Spatially offset Raman spectroscopy (SORS) is valuable for noninvasive bone assessment but requires a clearer understanding of how offset distances influence detection depth. To address this, our study devised a forward-adjoint Monte Carlo multi-layer (MCML) model to simulate photon paths in SORS, aiming to determine optimal offsets for various tissue types. We examined photon migration at offsets between 0 and 15 mm against layered phantoms of differing thicknesses and compositions to optimize the signal-to-noise ratio for bone layers. The findings highlight that optimal offsets are contingent on tissue characteristics: a metacarpal beneath 2.5 mm of tissue had an ideal offset of 6.7 mm, while a tibia with 5 mm of soft tissue required 10-11 mm. This precise calibration of SORS via MCML modeling promises substantial improvements in bone health diagnostics and potential for expansive medical applications.

Light field imaging technology for virtual reality content creation: A review

AbstractThe light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields.

Derivation and Internal Validation of a Novel Risk Assessment Tool to Identify Infants and Young Children at Risk for Post-Discharge Mortality in Dar es Salaam, Tanzania and Monrovia, Liberia

To derive and validate internally a novel risk assessment tool to identify young children at risk for all-cause mortality ≤60days of discharge from hospitals in sub-Saharan Africa. We performed a prospective observational cohort study of children aged 1-59months discharged from Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia (2019-2022). Caregivers received telephone calls up to 60days after discharge to ascertain participant vital status. We collected socioeconomic, demographic, clinical, and anthropometric data during hospitalization. Candidate variables with P<.20 in bivariate analyses were included in a multivariable logistic regression model with best subset selection to identify risk factors for the outcome. We internally validated our tool using bootstrapping with 500 repetitions. There were 1933 young children enrolled in the study. The median (IQR) age was 11 (4, 23) months and 58.7% were males. In total, 67 (3.5%) died during follow-up. Ten variables contributed to our tool (total possible score 82). Cancer (aOR 10.6, 95% CI 2.58, 34.6), pedal edema (aOR 6.94, 95% CI 1.69, 22.6), and leaving against medical advice (aOR 6.46, 95% CI 2.46, 15.3) were most predictive of post-discharge mortality. Our risk assessment tool demonstrated good discriminatory value (optimism corrected area under the receiver operating characteristic curve 0.77), high precision, and sufficient calibration. After validation, this tool may be used to identify young children at risk for post-discharge mortality to direct resources for follow-up of high-risk children.

Strain Measurement Technology and Precision Calibration Experiment Based on Flexible Sensing Fiber.

As the basic application of fiber optic sensing technology, strain measurement accuracy as a key index needs to be further calibrated and analyzed. In this paper, accuracy calibration experiments and the related analyses of two fiber-optic sensing technologies, the fiber-optic grating (FBG) and optical frequency domain reflectometry (OFDR), are carried out using a standard beam of equal strength and a mature resistive strain gauge (ESG). The fiber-optic single-point strain data for loading and unloading changes of the beams of equal strength show good continuity and linearity, with good cyclic stability, and the error in the strain test data is less than 2% after repeated loading. At the same time, using finite element theory to analyze the data and using the measured data error within 5%, a good strain test curve linearity is achieved and R2 is better than 0.998. After repeated loading and unloading tests, it is verified that the fiber grating and the distributed optical fiber in the strain test have good stability in repeatability accuracy. The calibration experiments and data analysis in this paper further illustrate the three sensing technologies in determining the strain test accuracy and the advantages and disadvantages of the indicators, and the development of the fiber optic sensing technology application provides basic technical support.

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

PurposeIn the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.Design/methodology/approachPrior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.FindingsThe average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.Originality/valueThis paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

Effectiveness of Ti-in-amphibole thermometry and performance of different thermometers across lower continental crust up to UHT metamorphism

Metabasites are important constituents of deep crustal sections and are the favored rock type for studying lower crustal amphibolite to granulite transitions. However, metapelites may develop a larger number of temperature-sensitive mineral assemblages and are particular useful when extreme ultrahigh temperature (UHT) conditions are envisaged. A recent calibration of the Ti-in-amphibole thermometer by Liao et al. (2021) was supposed to make thermometry on metabasites quick and easy to apply. However, their calibration is based on experiments which were not originally designed to investigate in detail the temperature dependence of Ti in amphibole. In addition, a possible effect of aTiO2 and/or pressure on the Ti content of amphibole was not fully taken into account. This resulted in a calibration uncertainty of ± 70 °C (2σ), much higher than that of other single-mineral thermometers. In this study we firstly test the newly calibrated Ti-in-amphibole thermometer across the mid to lower crustal section of the Ivrea–Verbano Zone (IVZ; NW Italy) and compare the performance of different thermometric techniques across the sequence. Ti-in-amphibole thermometry records increasing peak temperatures from amphibolite (600–700 °C), transition (750–800 °C) and granulite (850–950 °C) zones. Titanium content of amphibole may be modified by retrograde fluid influx returning temperatures c. 200–300 °C lower than in non-altered domains. The comparison reveals that Zr-in-rutile thermometer in pelitic granulites seems to be more prone to post-peak resetting than the Ti-in-amphibole thermometry in nearby mafic rocks. This behavior is also confirmed by amphibole analyses from other UHT localities, where the performance of Ti-in-amphibole thermometry is comparable with that of Al-in-orthopyroxene in pelitic granulites. However, Ti-in-amphibole temperatures are underestimated in rutile-bearing samples and this limitation is not solely restricted to rocks containing high H2O contents as previously thought. Derived constraints on the diffusivity of Ti through amphibole demonstrate the robustness of the Ti-in-amphibole thermometer to later thermal disturbances. However, ad-hoc experiments are still necessary to improve the accuracy and precision of calibration and to extend its applicability. This advance will make mafic granulites routine targets for studies devoted to understanding the regional extent of UHT metamorphism.

Camera-IMU extrinsic calibration method based on intermittent sampling and RANSAC optimization

Accurately calibrate the extrinsic parameters between a camera and an Inertial Measurement Unit (IMU) is a prerequisite for achieving sensor data fusion in visual-inertial navigation systems. Due to delays introduced by triggering, transmission, and other factors, the sampled times of the camera and IMU do not align with the system timestamps, leading to a decrease in the accuracy of extrinsic calibration. Existing calibration methods are unable to avoid the temporal misalignment between the camera and IMU. This paper introduces a novel intermittent calibration sampling method to address temporal misalignment challenges. The approach entails detecting motion-stillness transition points in the IMU data as keyframe segmentation criteria, thereby decoupling data acquisition from system timestamps and aligning it with the sensor’s motion process. Subsequently, the robot hand-eye calibration method is applied to the extrinsic calibration of the Camera-IMU, combining the Random Sample Consensus algorithm to filter the poses of the camera and IMU. This process achieves precise calibration of extrinsic parameters. Experimental validation shows that the algorithm proposed in this paper enhances calibration accuracy when compared to the widely used Kalibr Camera-IMU calibration toolbox. The rotational extrinsic parameter accuracy increased by 181.25%, and the translational extrinsic parameter accuracy improved by 168.33%. These findings provide effective technical means for precise measurement of Camera-IMU extrinsic parameters.

Laboratory and Numerical Investigation of Pre-Tensioned Reinforced Concrete Railway Sleepers Combined with Plastic Fiber Reinforcement.

This research investigates the application of plastic fiber reinforcement in pre-tensioned reinforced concrete railway sleepers, conducting an in-depth examination in both experimental and computational aspects. Utilizing 3-point bending tests and the GOM ARAMIS system for Digital Image Correlation, this study meticulously evaluates the structural responses and crack development in conventional and plastic fiber-reinforced sleepers under varying bending moments. Complementing these tests, the investigation employs ABAQUS' advanced finite element modeling to enhance the analysis, ensuring precise calibration and validation of the numerical models. This dual approach comprehensively explains the mechanical behavior differences and stresses within the examined structures. The incorporation of plastic fibers not only demonstrates a significant improvement in mechanical strength and crack resistance but paves the way for advancements in railway sleeper technology. By shedding light on the enhanced durability and performance of reinforced concrete structures, this study makes a significant contribution to civil engineering materials science, highlighting the potential for innovative material applications in the construction industry.

Simulated augmented reality-based calibration of optical see-through head mound display for surgical navigation.

Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration. This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality. The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method. The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation.

A MeerKAT Polarization Survey of Southern Calibration Sources

We report on full-Stokes L-band observations of 98 MeerKAT calibration sources. Linear polarization is detected in 71 objects above a fractional level of 0.2%. We identify ten sources with strong fractional linear polarization and low Faraday rotation measure that could be suitable for wide-band absolute polarization calibration. We detect significant circular polarization from 24% of the sample down to a detection level of 0.07%. Circularly polarized emission is seen only for flat spectrum sources α > −0.5. We compare our polarized intensities and Faraday synthesis results to data from the NVSS at 1400 MHz and the ATCA SPASS survey at 2300 MHz. NVSS data exist for 54 of our sources and SPASS data for 20 sources. The percent polarization and rotation measures from both surveys agree well with our results. The residual instrumental linear polarization for these observations is measured at 0.16%, and the residual instrumental circular polarization is measured at 0.05%. These levels may reflect either instabilities in the relative bandpass between the two polarization channels with either time or antenna orientation, or atmospheric/ionospheric variations with pointing direction. Tracking of the hourly gain solutions on J0408-6545 after transfer of the primary gain solutions suggests a deterioration of the gain stability by a factor of several starting about 2 hr after sunrise. This suggests that observing during the nighttime could dramatically improve the precision of polarization calibration.

DPCalib: Dual-Perspective View Network for LiDAR-Camera Joint Calibration

The precise calibration of a LiDAR-camera system is a crucial prerequisite for multimodal 3D information fusion in perception systems. The accuracy and robustness of existing traditional offline calibration methods are inferior to methods based on deep learning. Meanwhile, most parameter regression-based online calibration methods directly project LiDAR data onto a specific plane, leading to information loss and perceptual limitations. A novel network, DPCalib, a dual perspective view network that mitigates the aforementioned issue, is proposed in this paper. This paper proposes a novel neural network architecture to achieve the fusion and reuse of input information. We design a feature encoder that effectively extracts features from two orthogonal views using attention mechanisms. Furthermore, we propose an effective decoder that aggregates features from two views, thereby obtaining accurate extrinsic parameter estimation outputs. The experimental results demonstrate that our approach outperforms existing SOTA methods, and the ablation experiments validate the rationality and effectiveness of our work.

Ultrasonic-induced grafted lanthanum sulfide decorated multi-walled carbon nanotube onto bacterial cellulose applied for adsorption of pesticides in environmental waters

A new biosorbent was fabricated by modification of bacterial cellulose biopolymer grafted with lanthanum sulfide decorated carboxylated multiwall carbon nanotube (La2S3@MWCNT@BC). The sorbent was employed in a green alternative dispersive-solid phase extraction of a variety of 14 pesticides in environmental water samples. The analyses were performed using GC-µECD. The properties and structure of La2S3@MWCNT@BC nanocomposite were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and adsorption-desorption isotherms. The composition of the sorbent was also investigated to evaluate the adsorptive properties of its constituents. The impact of various parameters influencing extraction efficacies such as sorbent dose, adsorption time, sample pH, ionic strength, and desorption conditions was investigated. The method was validated by specificity, matrix effect % (-0.4 to -7.4), enrichment factor (4–10), limits of quantification (0.007–0.31 μg L−1), matrix-matched calibration linearity (0.01–200 µg L−1), determination coefficients (r2=0.9921–0.9998), and precision. The optimized method was applied for the analysis of multiclass pesticides in seven environmental and drinking waters and the recoveries were obtained in the 81–108 % range with RSDs of 2.5–4.7 %. This paper is the first report on the synthesis and use of La2S3@MWCNT@BC nanocomposite to extract pesticides from different water samples. The greenness of the procedure was evaluated by the AGREE protocols.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

Research on Dual-Grating Spacing Calibration Method Based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Combined with Hilbert Transform

The paper proposes a method for the calibration of spacing in dual-grating based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with Hilbert Transform (HT), referred to as Multiple ICEEMDAN-HT. This method addresses the potential impact of nonlinear factors on phase extraction accuracy, consequently on ranging precision in the homodyne interference of the dual-grating. Building upon the ICEEMDAN algorithm, the signal undergoes iterative decomposition and reconstruction using the sample entropy criterion. The intrinsic mode functions (IMFs) obtained from multiple iterations are then reconstructed to obtain the complete signal. Through a simulation and comparison with other signal decomposition methods, the repeatability and completeness of signal reconstruction by Multiple ICEEMDAN are verified. Finally, an actual dual-grating ranging system is utilized to calibrate the spacing of the planar grating. Experimental results demonstrate that the calibration relative error of the Multiple ICEEMDAN-HT phase unwrapping method can be reduced to as low as 0.07%, effectively enhancing the signal robustness and spacing calibration precision.