Position Error Research Articles

State-constrained control strategy for safe navigation trajectory tracking of hovercraft based on improved barrier Lyapunov function

This study addresses the challenge of trajectory tracking control for underactuated hovercraft by utilizing an improved integral barrier Lyapunov function to ensure safe and efficient navigation in the presence of uncertain dynamics and environmental disturbances. The research introduces an improved barrier Lyapunov function tailored for both constrained and unconstrained systems by integrating prescribed performance control (PPC) with a log-type barrier Lyapunov function (LBLF). This function effectively limits tracking errors in both position and heading to within a predetermined range. To ensure the hovercraft's navigational stability at high speeds, the paper presents a novel, time-varying integral barrier Lyapunov function (IBLF) with both unilateral and bilateral asymmetric constraints, specifically designed to control surge speed and yaw angular velocity. This ensures the maintenance of surge speed above the critical resistance peak speed and keeps the yaw angular velocity within a secure operational band. The study also introduces a finite-time disturbance observer (FTDO) to estimate the system's uncertainties. Theoretical analysis confirms that the tracking errors are ultimately uniformly bounded, establishing Lyapunov stability. Simulation results substantiate the superiority and efficacy of the proposed control strategy.

Modelling the weld cladding process to predict weld clad position and shape error

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough.

Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Transfer alignment method based on installation deviation angle estimation for array POS

Transfer alignment of array Position and Orientation System (POS) is the key technology to provide motion information for airborne earth observation high-precision imaging. Because the large deviation angle between main Inertial Measurement Unit (IMU) and slave IMU is difficult to accurately measure by existing methods, the transfer alignment accuracy will be seriously affected. Aiming at this issue, a transfer alignment method based on installation deviation angle estimation for array POS is proposed. This method includes two steps: pre-estimation and improved transfer alignment. First, two deviation angles are calculated by angular velocity and acceleration using quaternion optimization method respectively. The higher-precision deviation angle is fused by two deviation angles using their respective weights, which are calculated by Signal to Noise Ratios (SNRs). Second, the result of pre-estimation is used in transfer alignment. Residual errors of deviation angle are considered as state variables and be compensated online. Compared with the existing methods, the velocity, position and attitude errors calculated by this method are reduced by 15.4%, 31.7% and 66.5% respectively. The proposed method significantly reduces the adverse effect of installation deviation angle on measurement accuracy of array POS.

Design of Logistics Sorting Algorithm Based on Deep Learning and Sampling Evaluation

The advancement of automation and Internet of Things technology has bolstered the automation process in the logistics sector. To address the challenge of localizing and generating grasping positions for intelligent robots in logistics sorting, this study developed an algorithm for item localization. The algorithm relies on enhanced YOLOv3 target detection and instance segmentation technologies to design a position generation algorithm for the robotic arm, which was further refined using sampling evaluation. The experimental results showed that the research-improved target detection model performed better on different datasets in terms of F1 value, accuracy and Area under the Curve (AUC) metrics, with the highest values of 95.77%, 94.05%, and 91.30%, respectively, which was effective in localizing document-like parcels. Meanwhile, the instance segmentation algorithm with fused features took significantly lower values than other target detection algorithms in terms of average absolute value error and root mean square error. The accuracy rate and all-class average precision value were higher than other target detection models, and the fluctuation of the value taken was smaller, which was suitable for logistics parcel localization. The position generation model, based on a sampling evaluation, yielded significantly different values compared to other algorithms. The relative position error and absolute trajectory error indexes were all below 0.4. The combined indexes of grasping accuracy and error indicate the superior performance of the research-designed algorithms. They can effectively enhance the sorting effects of real logistics scenarios. This research contributes to the improvement of the automated sorting system through the use of visual robotic arm technology. Additionally, it encourages the development of logistics automation and the establishment of intelligent logistics factories.

Statement of Removal

A randomly fluctuated vortex-induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non-cooperative moving target. Definition 2.1, theorem 2.2 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard CRLB parameter quantifies the VIV effect. The compensation of vortex-induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight (LOS) errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 meters. It can be useful for the sub-aquatic internet of underwater things.

Verification of neuronavigated TMS accuracy using structured-light 3D scans

Objective. To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined. Approach. The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated. Main results. The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location. Significance. TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.

Subarray-based joint source localization in shallow water waveguide via subspace intersection

By the use of an underwater bottom-mounted Horizontal Linear Array (HLA), the two-dimensional joint localization technique and its accuracy are analyzed in this article. The proposed method takes advantage of arrival angles measured by a dual-subarray system consisting of a large-aperture HLA. Then, for the system, expressions for source position estimate and error variation are derived. Analysis of the Geometrical Dilution of Precision, which is a measure of localization error defined in Eq. (4), reveals that the localization error is mainly related to the source bearing, subarray interval and their measurement errors. In order to investigate the impact of the underwater waveguide, numerical simulations were carried out based on the normal mode theory. By using the distribution of acoustic intensity and structure of modal arrival, it is proved that the localization accuracy increases as the grazing angle and the subarray interval increase. However, when the measurement errors of source bearing and subarray interval increase, the localization accuracy will decrease. In addition, the obtained bearing estimation result provides an evident characteristic of beam spreading. When sources are near the end-fire direction of the dual-subarray system, significant bearing estimation error occurs, the negative effect of which is then presented. By applying the Subspace Intersection method, unbiased bearing estimation has been achieved, with relatively reduced computational complexity. Most of all, the improved localization performance is validated by experimental data collected in the South China Sea.

Accurate error compensation method for multi-axis parallel machine via singularized jacobi geometric parameter correction and coupling error evaluation

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

We present TimberTool (TTool v2.1.1), a software designed for woodworking tasks assisted by augmented reality (AR), emphasizing its essential function of the real-time localization of a tool head’s poses within camera frames. The localization process, a fundamental aspect of AR-assisted tool operations, enables informed integration with contextual tracking, facilitating the computation of meaningful feedback for guiding users during tasks on the target object. In the context of timber construction, where object pose tracking has been predominantly explored in additive processes, TTool addresses a noticeable gap by focusing on subtractive tasks with manual tools. The proposed methodology utilizes a machine learning (ML) classifier to detect tool heads, offering users the capability to input a global pose and utilizing an automatic pose refiner for final pose detection and model alignment. Notably, TTool boasts adaptability through a customizable platform tailored to specific tool sets, and its open accessibility encourages widespread utilization. To assess the effectiveness of TTool in AR-assisted woodworking, we conducted a preliminary experimental campaign using a set of tools commonly employed in timber carpentry. The findings suggest that TTool can effectively contribute to AR-assisted woodworking tasks by detecting the six-degrees-of-freedom (6DoF) pose of tool heads to a satisfactory level, with a millimetric positional error of 3.9 ± 1 mm with possible large room for improvement and 1.19 ± 0.6° for what concerns the angular accuracy.

Online Self-Calibration of Cable-Driven Parallel Robots Using Covariance-Based Data Quality Assessment Metrics

Abstract This paper presents an algorithm to perform self-calibration of cable-driven parallel robots (CDPRs), where the CDPR's end-effector pose is estimated in conjunction with the calibration of biases in the CDPR's measurements. Two new metrics, known as the position dilution of precision (PDOP) and orientation dilution of precision (ODOP) are introduced as a means to quantify the quality of data collected with regards to self-calibration. These metrics are based on a covariance matrix that is computed online as part of the proposed self-calibration algorithm, which results in the PDOP and ODOP directly corresponding to the standard deviation of the position and orientation errors, respectively. These metrics are used to intuitively select which data points contribute to improved calibration, resulting in a computationally-efficient algorithm requiring few data points to maintain accurate calibration. Additionally, the PDOP and ODOP provide a means to assess when sufficient calibration data has been collected. Numerical results involving an inverse kinematic simulation with rigid cables and a dynamic simulation with flexible cables indicate that the proposed algorithm is capable of performing self-calibration in a computationally-efficient manner. Moreover, the simulation results indicate that the proposed PDOP and ODOP metrics result in smaller position and orientation errors when used to prune the data set compared to the observability indices found in the literature. Accuracy of the proposed algorithm is also confirmed through experiments when compared to ground-truth pose data.

Mixed reality guided root canal therapy.

Root canal therapy (RCT) is a widely performed procedure in dentistry, with over 25 million individuals undergoing it annually. This procedure is carried out to address inflammation or infection within the root canal system of affected teeth. However, accurately aligning CT scan information with the patient's tooth has posed challenges, leading to errors in tool positioning and potential negative outcomes. To overcome these challenges, a mixed reality application is developed using an optical see-through head-mounted display (OST-HMD). The application incorporates visual cues, an augmented mirror, and dynamically updated multi-view CT slices to address depth perception issues and achieve accurate tooth localization, comprehensive canal exploration, and prevention of perforation during RCT. Through the preliminary experimental assessment, significant improvements in the accuracy of the procedure are observed. Specifically, with the system the accuracy in position was improved from 1.4 to 0.4 mm (more than a 70% gain) using an Optical Tracker (NDI) and from 2.8 to 2.4 mm using an HMD, thereby achieving submillimeter accuracy with NDI. 6 participants were enrolled in the user study. The result of the study suggests that the average displacement on the crown plane of 1.27 ± 0.83 cm, an average depth error of 0.90 ± 0.72 cm and an average angular deviation of 1.83 ± 0.83°. Our error analysis further highlights the impact of HMD spatial localization and head motion on the registration and calibration process. Through seamless integration of CT image information with the patient's tooth, our mixed reality application assists dentists in achieving precise tool placement. This advancement in technology has the potential to elevate the quality of root canal procedures, ensuring better accuracy and enhancing overall treatmentoutcomes.

1067 Accuracy of an Augmented Reality Spine Surgery Guidance System With Stereoscopic Targeting Head Mounted Display Compared to Standard Computer Navigation, Robotic Systems, and Existing AR Systems

INTRODUCTION: Augmented Reality (AR) offers the benefit of improved hand-eye coordination by accurately superimposing 3D stereoscopic displays for surgical instruments onto anatomic landmarks. METHODS: 120 pedicle screws were implanted using an AR system (OnPoint AR, OnPoint Surgical, Bedford, MA) (thoracic 50, lumbosacral 70; pre-op CT 40, O-arm 80). Heary grading was applied by an independent radiologist assessing the degree of pedicle breaches. 3D measurements of positional error (PE) and angular error (AE) were performed by comparing post-operative CT scans of implanted screws with planned trajectories. Statistical analyses compared PE and AE for OnPoint AR with the available values for other systems using an unequal variance t-test method. RESULTS: 113 screws placed demonstrated Heary grade 1 accuracy. 6 screws had a larger diameter than the pedicle and were planned with in-out-in technique with Heary grade 2 accuracy. One screw demonstrated a lateral breach (grade 2) caused by a loose connection to the screwdriver. PE and AE (mean ± 1SD) for OnPoint AR were the following: for intra-operative spin 1.1mm±0.5mm and 1.3°±0.5°; for pre-operative CT 1.9mm±1.2mm and 2.2°±1.3°. Percent increment in PE and AE for Medtronic Stealth Station, Brainlab navigation, Stryker nav3i, Medtronic Mazor X robot, Globus Excelsius robot, Augmedics Xvision, and Novarad VisAR compared to OnPoint AR ranged between 40–80%, with differences being highly significant for all comparisons. CONCLUSIONS: PE and AE for placing pedicle screws are statistically significantly smaller for the novel AR system compared to existing navigation, robotic, and AR systems. A positional error of 1.1mm and angular error of 1.3° makes this technology particularly suited for small pedicles, challenging anatomy, and MIS techniques.

Thermal error modelling and compensation of CNC lathe feed system based on positioning error measurement and decoupling

Thermal error modeling and compensation considering of the measured positioning error decoupling for the lathe with a slant bed is proposed. Due to the complex coupling factors of thermal error, the current compensation methods exhibit limited applicability and generalization. The linear fitting method is used to decouple geometric error and thermal error from the positioning error. The motion heating and non-motion heating experiments of X-direction feed system were carried out to investigate the principle of thermal error generation and the impact factors. The multiple linear regression model of the thermal error was established based on the analysis of the thermal error measurement results. To verify the effectiveness of the proposed model, the thermal error at two working conditions was predicted, and then the error compensation was carried out. The results show that the positioning error is reduced by more than 84% and 92% with the present method compared with that without compensation. This research provides effective method for the generation principle and compensation of thermally induced errors of lathe with a slant bed.

Error Modeling and Accuracy Analysis of Parallel Robots for Spacecraft Docking Motion Simulation System

Background:: A large number of on-orbit docking dynamics experiments are conducted in the spacecraft docking motion simulation system with the development of Chinese space science and technology, and therefore, the requirement for high-precision parallel robots has increased. The pose accuracy of parallel robots is one of the most important problems in this application. Objective:: In order to evaluate the pose accuracy of parallel robots in advance, a comprehensive pose accuracy analysis method considering major error sources is presented in this paper, and the influence of major error sources on the pose accuracy is also investigated to summarize the statistics and propagation characteristics of the pose error. Methods:: The first-order error model for the parallel robot is established based on a generalized error model for each hydraulic cylinder and a sensitivity analysis method. Using the error model, a statistical approach to the parallel robot pose accuracy analysis is presented, and the influence of different parameter errors and different poses on the pose accuracy of the parallel robot is investigated. Sensitivity analysis is applied to evaluate the contribution of each parameter error to the position and orientation error of the parallel robot. An automated pose accuracy analysis program that computes and graphically displays the position and orientation error distributions and the sensitivity analysis results is developed. Results: The statistical analysis results of the influence of different parameter errors and different poses on pose accuracy are obtained by using the automated pose accuracy analysis program. The means of the position and orientation errors are close to zero. The standard deviations in the x and y directions are larger than those in the z direction, and these standard deviations are amplified with the increase of the parameter errors. For the given elevation, sensitivity analysis to various parameter errors is performed. It is found that the length error sensitivities of the hydraulic cylinders are less than one, and position error sensitivities of the hook joints A4, A5, and A6 are much greater than those of hook joints A1, A2 and A3. Conclusion:: The elemental error sources belong to one of two groups, i.e., those affecting the hydraulic cylinder length and those affecting the hook joints. The distributions of the position and orientation errors are consistent with statistics theories. The parallel robot is more sensitive to the errors affecting the hook joints than those affecting the hydraulic cylinder length. These will help the designers and users of the parallel robot understand the statistics and propagation characteristics of the pose error. Some recent patents on error modeling and kinematic calibration of parallel robots are also discussed in this article.

A Robot Positional Error Compensation Method Based on Improved Kriging Interpolation and Kronecker Products

This paper proposes a stable and high-accuracy model-free calibration method for unopened robotic systems, which can significantly improve the robot positional accuracy. Two improvements are made to the existing kriging-based error compensation method to achieve robustness/practicality enhancement goals: (1) The semi-variogram model is solved using the sequential quadratic programming (SQP) algorithm, and the distance weight coefficients are added to the objective function. The accuracy of semi-variogram modeling is guaranteed, thereby improving the performance and stability of error compensation. (2) The hand-eye (the pose matrix between the robot base and the laser tracker) and the tool center point (TCP) position are calibrated based on Kronecker products without relying on the spatial analyzer (SA) software to construct the robot base coordinate system separately. Thus, the calibration accuracy and efficiency are significantly improved. Experiments have been conducted, and the results reveal that the proposed method can significantly reduce the robot positional error. Furthermore, the proposed approach performs better than the existing kriging-based error compensation method without Kronecker products. The maximum, mean, and root mean square (RMS) values of the absolute positional errors are reduced by 34.48%, 8.09%, and 10.05%, respectively.

VLBI position variability of AGNs is inversely correlated with their photometric variability

Aims. The stability of the International Celestial Reference Frame (ICRF), realized through geodetic very long baseline interferometry (VLBI) positions of thousands of extragalactic objects, is dependent on the individual positional stability of these objects. It has been recently shown that the prevalence of offsets between the VLBI positions of ICRF objects and their Gaia optical positions, which limit the optical-radio reference frame tie, is inversely correlated with optical photometric variability, suggesting that photometrically variable objects may be more positionally stable. In this work, we determine the relationship between VLBI position stability of ICRF objects and optical-radio position offsets as well as optical photometric variability. Methods. We created multi-epoch geodetic VLBI solutions for a sample of 520 ICRF sources that have sufficient data to determine the variability in their VLBI positions over time. We compared this position variability with the fractional photometric variability provided by the Gaia extragalactic source catalog, the Gaia-ICRF optical-radio position offsets, the uncertainty-normalized position offsets, and optical BP-RP color as well as with possible confounders such as optical magnitude, VLBI/Gaia position error, and redshift. We determined the relationship between VLBI position stability and γ-ray detection by the Fermi Large Area Telescope (LAT), and we determined how the VLBI position and optical flux variabilities correlate with the spectral classification of our sample, considering flat spectrum radio quasars (FSRQs), quasi-stellar objects, BL Lacs, Seyfert, and gigahertz-peaked spectrum radio sources or compact-steep-spectrum radio sources. Results. We found that VLBI astrometric variability is (i) negatively correlated with optical flux variability, (ii) positively correlated with optical-radio offsets, (iii) negatively correlated with optical color index BP-RP, and (iv) negatively correlated with γ-ray detection. We also found that the most positionally stable sources are among the FSRQ and BL Lac classes. In other words, redder, photometrically variable sources have the most stable VLBI positions, the smallest optical-radio position offsets, and the highest rate of γ-ray detection, and these sources tend to be spectrally classified as blazars. Conclusions. Our results are consistent with the most positionally stable sources being blazars, a class of object in which the jet is oriented close to the line of sight and where relativistic beaming increases photometric variability and minimizes the projected offset between the optical and radio positions. Our study should therefore orient future geodetic VLBI observing programs preferentially toward sources with high photometric variability because these sources are predicted to have better VLBI position stabilities and smaller optical-radio position offsets, improving the stability of the celestial reference frame axes.

Multiple Discrete Adaptive Filter Based Position Error Reduction for Sensorless IPMSM Drives

The inverter nonlinearity and the flux spatial harmonics will lead to noticeable (6κ±1) harmonics in the observed back electromotive force (EMF), causing (6κ) pulsations in the rotor position estimation error and deteriorating the performance of sensorless interior permanent magnet synchronous motor (IPMSM) drives. To improve the sensorless IPMSM control accuracy, a multiple discrete adaptive filter (MDAF) embedded between sliding mode observer (SMO) and normalized quadrature phase-locked loop (PLL) is adopted to solve the above problem in this paper. The MDAF consists of a fundamental extraction block and a harmonic cancellation block. Harmonic cancellation works similarly to fundamental extraction, simply by removing the dominant 5th and 7th harmonics from the distorted back-EMF in a frequency multiplicative manner. The proposed algorithm is designed directly in the discrete domain, avoiding the complex discretization process. The open-loop amplitude-frequency characteristic curves show that the method is able to achieve infinite gain exactly at the expected frequency, thus allowing closed-loop zero steady-state error tracking of the back-EMF and providing higher precision for harmonic estimation. The excellent performance of the suggested method is validated based on a 1.5kW sensorless IPMSM control system.

Unveiling Starlink LEO Satellite OFDM-Like Signal Structure Enabling Precise Positioning

This letter unveils the unknown structure of Starlink low Earth orbit (LEO) satellites' orthogonal frequency division multiplexing (OFDM)-like reference signals (RSs). The spectrum of Starlink's dowlink signals is presented, and the frame length is estimated. A blind receiver is proposed, which acquires via a sequential generalized likelihood ratio test multiple satellites, estimates their RSs and respective Doppler, and tracks their carrier and code phases. Experimental results are presented showing six tracked Starlink LEO satellites, three of which transmitted pure tones, while the other transmitted OFDM-like signals. The achieved horizontal positioning error with the six satellites was 6.5 m.

Self-Calibration of INS/Odometer Integrated Navigation System With a Large Initial Position Error

Self-Calibration of INS/Odometer Integrated Navigation System With a Large Initial Position Error