Attitude Measurement Research Articles

Research on intelligent cutting control technology of transverse moving machine for large cross-section roadheader

With the continuous development of the coal mine industry in our country, the cross-section of the roadway is getting larger and larger. The widely used EBZ series roadheader cannot complete the roadway cutting operation with a large cross-section at one time, so it usually needs to move the equipment left and right to complete all its cutting tasks. This paper studies the intelligent control technology of large cross-section roadway cutting, which mainly solves the positioning and attitude determination technology of roadheader in the large cross-section, and offers an accurate shape-cutting control technology and a path planning technology using a lateral moving machine. This paper proposes to combine the signals of laser sensor and fiber-optic inertial navigation system to form a combined positioning and attitude determination system, so as to make use of the advantages and avoid the disadvantages of their respective systems, and improve the accuracy of roadheader positioning and attitude determination. A swing kinematics model of the cutting arm is established, based on which the precise motion control of the first roadway section cutting and the second roadway section cutting can be realized. A key parameter calculation model of roadheader lateral moving machine is put forward to calculate the operation parameters for roadheader lateral moving machine path planning. Finally, the experimental research on intelligent cutting control technology of large cross-section roadheader is carried out in 12307 intelligent heading face of Wangjialing Coal Mine. The results show that the positioning error of the combined positioning and attitude determination system proposed in this paper is between 50-90 mm in the X-axis direction and between 25-45 mm in the Y-axis direction. The average attitude measurement error of heading angle is between 0.5 and 1.5°. This makes the technology appropriate for engineering use. The studied cutting control system achieves a cutting error of 25-50 mm for the first section, and a cutting error of 40-90 mm for the complete section. The complete section cutting error meets the ±100 mm cutting error requirement required in engineering. The intelligent cutting control technology of the lateral moving machine of the large-section tunnel boring machine studied in this paper can realize accurate cutting of the large-section tunnel of 5.6 meters. The research results of this paper provide a reference for the intelligent construction of heading faces.

Improving the accuracy of dynamic inclination measurement by machine learning

With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

Teachers as thinkers, not feelers: Clarifying the definition and measurement of attitude in teachers’ acceptance of online teaching

This mixed-methods study attempted to clarify the definition and measurement of attitude in teachers' acceptance of online teaching. A combined quantitative and qualitative data collection approach was employed, involving 2,432 Chinese university teachers in the survey. The results supported the appropriateness and necessity of adopting an evaluation-focused attitude rather than affect-focused attitude for explaining teachers' intention to conduct online teaching, suggesting teachers are “thinkers” rather than “feelers” when forming their attitudes towards online teaching. These findings highlight the role of cost-benefit appraisal in determining teachers’ adoption and implementation of online teaching, thus potentially influencing teaching quality of higher education.

Assessment of the Knowledge, Attitudes, and Practices for Optimal Oral Health among Adults in Benin City, Nigeria: A Cross-sectional Study

Introduction: Oral health is crucial for overall well-being, involving the maintenance of good oral hygiene to prevent diseases. Despite global progress, gaps persist in developing countries, leading to higher oral disease prevalence. Factors such as awareness, access, cultural beliefs, and dental phobia significantly impact oral health. Knowledge, attitudes, and practices also play critical roles. Objective: To assess the knowledge, attitudes, and practices related to the oral health of adults in Benin City, Edo State. Methods: This cross-sectional study included 198 adults attending a dental outreach program in three local government areas (Ikpoba-Okha, Oredo, and Egor) in Benin City, Edo State. A sample size of 200 was determined using the Cochrane formula. A researcher-administered proforma was used for data collection, assessing socio-demographics, oral health knowledge, attitudes, and practices. A validated questionnaire was used for knowledge assessment, and a Likert scale was employed for attitude measurement. The Simplified Oral Hygiene Index (OHI-S) and the Decayed, Missing, and Filled Teeth (DMFT) index were used for clinical examination. Data were analyzed using IBM® SPSS® Statistics version 25. Results: The mean oral health knowledge, attitudes, and practices among respondents were 57.5%, 83.1%, and 55.0%, respectively. Conclusion: The study revealed fair knowledge and practices but good attitudes among respondents. Notable gaps exist in oral hygiene knowledge and practices, although the positive attitudes observed may support effective oral health promotion efforts. Update Dent. Coll. j: 2024; 14(2): 3-8

Research on Precise Attitude Measurement Technology for Satellite Extension Booms Based on the Star Tracker

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Remote marine precise point positioning with baseline length and troposphere-constrained models of the receivers for the oceanographic research vessel

ABSTRACT Research vessels are typically equipped with multiple receivers for positioning and attitude measurement; however, there is no data interaction or model fusion to implement the Precise Point Positioning (PPP) for these receivers. Therefore, a Constrained PPP (CPPP) is established by using the baseline length and tropospheric constraints of two receivers to improve the vessel’s remote marine positioning performance. Based on two integrated Real-Time Kinematic (RTK) receivers, marine experiments are conducted at a distance of 300–600 km from land. Compared to the ground environment, the Root Mean Square error (RMS) of the multipath and the STandard Deviation (STD) of the carrier-to-noise ratio in the marine environment are increased by 2.87 and 2.6 times, respectively. The length constraints reduce the RMS of positioning in the East, North, and Up (ENU) directions by about 0.053 (67%), 0.020 (34%), and 0.054 m (34%), respectively, and also rapidly recover positioning after interruptions, achieving positioning with errors in the ENU-directions of 10, 15, and 20 cm, respectively, within 3 minutes. When the baseline length and tropospheric constraints are combined, with or without the addition of the Zenith Wet Delay bias (dZWD), the average STD can be reduced by about 0.060 m (43%) and the accuracy of the up-positioning can be significantly improved. The correlation of dZWD to the up-positioning deviation of du=−2.161*dZWD is obtained by simulation. Since the acquisition of dZWD depends on the PPP accuracy, it is recommended that dZWD be set to compensate for unmodeled receiver errors when the STD of the up-positioning is superior to 0.06 m.

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

Intelligent analysis and measurement of semicircular canal spatial attitude.

The primary aim of this investigation was to devise an intelligent approach for interpreting and measuring the spatial orientation of semicircular canals based on cranial MRI. The ultimate objective is to employ this intelligent method to construct a precise mathematical model that accurately represents the spatial orientation of the semicircular canals. Using a dataset of 115 cranial MRI scans, this study employed the nnDetection deep learning algorithm to perform automated segmentation of the semicircular canals and the eyeballs (left and right). The center points of each semicircular canal were organized into an ordered structure using point characteristic analysis. Subsequently, a point-by-point plane fit was performed along these centerlines, and the normal vector of the semicircular canals was computed using the singular value decomposition method and calibrated to a standard spatial coordinate system whose transverse planes were the top of the common crus and the bottom of the eyeballs. The nnDetection target recognition segmentation algorithm achieved Dice values of 0.9585 and 0.9663. The direction angles of the unit normal vectors for the left anterior, lateral, and posterior semicircular canal planes were [80.19°, 124.32°, 36.08°], [169.88°, 100.04°, 91.32°], and [79.33°, 130.63°, 137.4°], respectively. For the right side, the angles were [79.03°, 125.41°, 142.42°], [171.45°, 98.53°, 89.43°], and [80.12°, 132.42°, 44.11°], respectively. This study successfully achieved real-time automated understanding and measurement of the spatial orientation of semicircular canals, providing a solid foundation for personalized diagnosis and treatment optimization of vestibular diseases. It also establishes essential tools and a theoretical basis for future research into vestibular function and related diseases.

Dynamic Attitude Inertial Measurement Method for Typical Regions of Deck Deformation

Due to the deformation of ships, it becomes difficult to ensure the accuracy of attitude measurement in typical areas on the deck, which seriously impacts the safety and operational efficiency of shipborne equipment. To address this issue, this paper presents a parameter identification method for dynamic deformation models based on angle increment differences and introduces the related vector machine (RVM) algorithm for online estimation of dynamic deformation model parameters. In view of the truncation error and non-Gaussian noise of the model, this article proposes a dynamic attitude measurement method based on model predictive filtering (MPF), constructs a dynamic measurement model using Rodrigues parameters in an inertial frame, and designs a maximum correlation entropy (MCE) robust filter to achieve robust estimation of deck dynamic deformation. The performance of the method is verified through simulation analysis and shipborne experiments. The comparative results indicate that, compared with existing methods, the proposed improved deck dynamic attitude measurement algorithm based on model prediction (IDAM) can substantially enhance the accuracy of attitude measurement in the presence of deck dynamic deformations.

Improved YOLOv5 Network for High-Precision Three-Dimensional Positioning and Attitude Measurement of Container Spreaders in Automated Quayside Cranes.

For automated quayside container cranes, accurate measurement of the three-dimensional positioning and attitude of the container spreader is crucial for the safe and efficient transfer of containers. This paper proposes a high-precision measurement method for the spreader's three-dimensional position and rotational angles based on a single vertically mounted fixed-focus visual camera. Firstly, an image preprocessing method is proposed for complex port environments. The improved YOLOv5 network, enhanced with an attention mechanism, increases the detection accuracy of the spreader's keypoints and the container lock holes. Combined with image morphological processing methods, the three-dimensional position and rotational angle changes of the spreader are measured. Compared to traditional detection methods, the single-camera-based method for three-dimensional positioning and attitude measurement of the spreader employed in this paper achieves higher detection accuracy for spreader keypoints and lock holes in experiments and improves the operational speed of single operations in actual tests, making it a feasible measurement approach.

Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor’s robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only.

Extended Kalman filter-based robust roll angle estimation method for spinning vehicles

Attitude measurement is a basic technique for monitoring vehicle motion states and safety. The spin motion of a vehicle couples the attitude angles with each other, which has an impact on the navigation and control of the vehicle. Global navigation satellite system (GNSS) signals-based roll angle measurement methods are important for vehicle attitude measurement. Most of existing studies use continuous signal power, but the case of loop lock loss leading to discontinuous power reception has not been considered. A robust estimation method for the roll angle based on the Tukey weight function is proposed to improve the measurement accuracy in cases of discontinuous reception. The characteristics of the GNSS signals, the geometric relationship between the signal power and roll angle of the vehicle are discussed. By installing a GNSS receiver with a single patched antenna on a rotating platform with a controllable rolling speed, the proposed method was verified by experiments. The robust estimation errors of different weight functions are analyzed. According to the characteristics of the gross measurement errors, a robust estimation method of multisatellite power observations is proposed to obtain a high-precision and stable estimation of the vehicle roll angle. The results show that the proposed algorithm can improve the accuracy of roll angle estimation even with gross measurement errors. As a result of the experiments, the estimation errors of the algorithm are 6.57° at a confidence level of 68 % and 15.49°at the confidence level of 95 %. In contrast, they are 11.38° and 37.31° for the traditional LS method. Moreover, the estimation accuracy of the algorithm is not significantly correlated with the vehicle rotational speed. Therefore, the vehicle roll angle can be estimated with high accuracy under a variety of rotational speeds.

A Pilot Study Approach to Assessing the Reliability and Validity of Relevancy and Efficacy Survey Scale

This paper is based on the pilot study that aims to assess the reliability and validity of the attitude measurement scale towards the relevance of mathematical knowledge of real analysis for secondary-level mathematics instruction. Research tool validation and reliability estimation are challenging due to the complexity of its procedure. This study establishes procedural guidance for piloting and tool validation. The survey method was used to conduct the pilot study. Thirty participants filled in the 45 indicators of relevance measurement and 10 mathematical efficacy measurement indicators. The reliability was assessed with Cronbach's Alpha formula while the validity of the questionnaire was examined through item-total correlation with each item by using Pearson's formula. The content validity index was also calculated based on the expert judgment method. The reliability indicator was found to be more than 0.70 and the validity indicator falls in the acceptable range. Therefore, developed scale is valid and reliable to measure the teachers' attitudes on the relevancy of mathematical knowledge of real analysis of school's mathematics instruction based on pilot study results. Furthermore, this study guides the pilot study procedure for survey research and especially benefits those looking to validate their Likert-type survey scale in any research.

Dual-Model Derailment Detection Algorithm Based on Variational Bayesian Kalman Filtering.

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection.

Evaluating Item Response Format and Content Using Partial Credit Trees in Scale Development

Abstract The type of response options selected for items on a survey, along with how many response options to include and whether to allow neutral midpoints, impacts data obtained from survey collections and the interpretations made using the results. Further, if subgroups within a population (e.g., racial/ethnic, gender, age) interpret response options differently, this variance can artificially inflate non-significant differences or mask true differences between groups. In this study, we apply two recursive partitioning procedures for investigating differential item functioning (DIF) in an experiment evaluating seven item response formats (five levels of an agree–disagree [AD] format and two levels of an item-specific [IS] format). Partial credit tree procedures allow for the evaluation of multiple covariates without prespecifying subgroups to be compared. We applied the procedures to items measuring adults’ attitudes toward legal abortion and all response formats functioned without DIF for age, gender, race, education, and religion when evaluated using global DIF screening approaches. Item-focused analyses indicated that odd-numbered response formats were less susceptible to content-based DIF. The combination of psychometric properties indicated that five-point AD and IS formats may be preferable for abortion attitude measurement based on the screening procedures conducted in this study.

A Near-Vertical Well Attitude Measurement Method with Redundant Accelerometers and MEMS IMU/Magnetometers

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45° and 27.09° to 5.7° and 0.61°, respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells.

MULTIDIMENSIONAL SCALE TO MEASSURE STAKEHOLDERS' ATTITUDES ON COMMUNITY PARTICIPATION IN TANK MANAGEMENT

Attitude is an important psychological determinant of the behavior of an individual. Measurement of such attitude of tank users towards community participation is essential for tank management. Many social researchers measure multidimensional variables, such as community participation, by aggregating the scores of individual dimensions / sub-dimensions. These dimensions / sub-dimensions are highly correlated and have inherent problems of multicollinearity which causes attenuation in measurement. Community participation is a multidimensional variable, whereas it is being measured as if it was a uni-dimensional variable. The data on community participation have been collected from randomly selected 40 experts related irrigation, rural development, extension, agriculture and panchayat raj departments. The collected data exposed to statistical tools like item total correlation, t-test followed by principal component analysis. The scale was standardized by testing its reliability (r = 0.83) and validity (validify coefficient= 0.91). The final scale consisting of 39 statements (29 positive and 10negative) categorized in to 7 dimensions i.e. Resource sharing behavior (6), Collective behavior (5), Capacity building (6), Responsibility (6), Involvement (6), Collectivism (4) and Self-perception (6) was presented to respondents with instructions to indicate their agreement on five-point continuumviz. strongly agree, agree, undecided, disagree, and strongly disagree with the weightages of 5, 4, 3, 2 and 1 for positive statements and vice versa for negative statements, The attitude score of each respondent is obtained by summing up the scores on all attitude statements.

3D position and pose measurement based on coded light field

AbstractHigh‐precision relative position and attitude measurement technology has a wide range of applications in aerospace and industrial production. Currently, the commonly used method for measurement is based on visual cooperative signs. However, its accuracy significantly decreases as the distance increases. Therefore, a relative positioning system is designed based on light field spatial coding and visual recognition. The projector emits spatially encoded structured light within the coverage of the light field, while the receiving end captures, identifies, measures the code, and calculates its pose in the light field coordinate system. Compared with the traditional measurement method, the measurement accuracy of this system does not decrease greatly with the increase in distance, the measurement distance can be adjusted in real‐time and does not depend on an external light source. By changing the projection pattern with different resolutions without changing hardware systems, it can adjust effective measurement distance accordingly. Theoretical and experimental results show that the proposed method can maintain measurement accuracy with the increase in distance.

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.