Angle Estimation Research Articles

Active rack control of steer-by-wire systems for vehicle lateral stability

This paper describes a stability control strategy based on a steer-by-wire system to improve the lateral stability and manoeuvrability of vehicles by integrating individual chassis control modules, such as electronic stability control (ESC) and active front steering (AFS). Because of the uncertainty of cornering stiffness, an adaptive sliding-mode control is implemented without using the cornering stiffness value. An adaptive parameter is used instead of the cornering stiffness in the slip angle estimation and the lateral stability control. An unscented Kalman filter is used to estimate the slip angle and adaptive sliding mode control is used in lateral stability control. An equivalent desired command is calculated in lateral stability control. An integrated AFS and four individual wheel-braking controls were utilised to achieve an optimal distribution of longitudinal and lateral tyre forces, aiming to attain the desired command for lateral stability. Optimal coordination of the control authority for the AFS and the ESC has been chosen to minimise the force of each tyre. Computer simulations and vehicle tests of a closed-loop driver–vehicle–controller system subjected to a double-lane change have been carried out to verify that the proposed control strategy works.

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Twin-in-the-loop state estimation for vehicle dynamics control: Theory and experiments

In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile or even not available. Therefore, real-time estimation techniques need to be used. The previous approach suffers from two main drawbacks: (i) the approximations due to model mismatch might jeopardize the performance of the final estimation-based control; (ii) each new estimator requires the calibration from scratch of a dedicated model. In this paper, we propose a twin-in-the-loop scheme, where the ad-hoc model is replaced by an accurate full-fledged simulator of the vehicle, typically available to vehicles manufacturers and suitable for the estimation of any on-board variable, coupled with a compensator within a closed-loop observer scheme. Given the black-box nature of the digital twin, a data-driven methodology for observer tuning is developed, based on Bayesian optimization. The effectiveness of the proposed estimation method for the estimation of vehicle states and forces, as compared to traditional model-based Kalman filtering, is experimentally shown on a dataset collected with a sport car. In summary, the proposed approach achieves, in the most aggressive driving scenarios, an average improvement of 0.5° for the side-slip angle estimation, and of more than 500 N for the front lateral forces estimation.

Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps.

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.

Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation

BackgroundGeneralized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model.ObjectiveThe proposed model is designed to provide the most accurate joint angle measurements in EDS appraisals. Using a hybrid of CNNs and HyperLSTMs in the pose estimation module of HybridPoseNet offers superior generalization and time consistency properties, setting it apart from existing complex libraries.MethodologyHybridPoseNet integrates the spatial pattern recognition prowess of MobileNet-V2 with the sequential data processing capability of HyperLSTM units. The system captures the dynamic nature of joint motion by creating a model that learns from individual frames and the sequence of movements. The CNN module of HybridPoseNet was trained on a large and diverse data set before the fine-tuning of video data involving 50 individuals visiting the EDS clinic, focusing on joints that can hyperextend. HyperLSTMs have been incorporated in video frames to avoid any time breakage in joint angle estimation in consecutive frames. The model performance was evaluated using Spearman’s coefficient correlation versus manual goniometry measurements, as well as by the human labeling of joint position, the second validation step.OutcomePreliminary findings demonstrate HybridPoseNet achieving a remarkable correlation with manual Goniometric measurements: thumb (rho = 0.847), elbows (rho = 0.822), knees (rho = 0.839), and fifth fingers (rho = 0.896), indicating that the newest model is considerably better. The model manifested a consistent performance in all joint assessments, hence not requiring selecting a variety of pose-measuring libraries for every joint. The presentation of HybridPoseNet contributes to achieving a combined and normalized approach to reviewing the mobility of joints, which has an overall enhancement of approximately 20% in accuracy compared to the regular pose estimation libraries. This innovation is very valuable to the field of medical diagnostics of connective tissue diseases and a vast improvement to its understanding.

Stretchable glove for accurate and robust hand pose reconstruction based on comprehensive motion data

We propose a compact wearable glove capable of estimating both the finger bone lengths and the joint angles of the wearer with a simple stretch-based sensing mechanism. The soft sensing glove is designed to easily stretch and to be one-size-fits-all, both measuring the size of the hand and estimating the finger joint motions of the thumb, index, and middle fingers. The system was calibrated and evaluated using comprehensive hand motion data that reflect the extensive range of natural human hand motions and various anatomical structures. The data were collected with a custom motion-capture setup and transformed into the joint angles through our post-processing method. The glove system is capable of reconstructing arbitrary and even unconventional hand poses with accuracy and robustness, confirmed by evaluations on the estimation of bone lengths (mean error: 2.1 mm), joint angles (mean error: 4.16°), and fingertip positions (mean 3D error: 4.02 mm), and on overall hand pose reconstructions in various applications. The proposed glove allows us to take advantage of the dexterity of the human hand with potential applications, including but not limited to teleoperation of anthropomorphic robot hands or surgical robots, virtual and augmented reality, and collection of human motion data.

Analytical quaternion-based bias estimation algorithm for fast and accurate stationary gyro-compassing

This work introduces a novel approach to Strapdown Inertial Navigation System (SINS) alignment, distinct from recursive methods like Kalman filtering. The proposed methodology expedites bias error calculations by utilizing quaternion-based analytical relationships, which bypasses the slow convergence behavior associated with recursive algorithms, particularly in azimuth angle error estimation. In addition, the proposed approach demonstrates comparable accuracy to traditional fine alignment methods. Simulations and experiments validate that in contrast to the 10-min time requirement of traditional fine alignment methods (for azimuth angle estimation in stationary conditions), the proposed approach achieves the same accuracy within 20 s. However, limitations exist as the algorithm is applicable only in stationary conditions, and necessitating a high-grade IMU capable of measuring the earth’s rotation rate.

DOA Estimation Based on Virtual Array Aperture Expansion Using Covariance Fitting Criterion

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output’s covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data.

Antenna and attitude modeling of modernized GLONASS satellites

Next to the legacy frequency division multiple access (FDMA) signals in the L1 and L2 band, modernized GLONASS satellites of the M+, K1, and K2 generation also transmit new code division multiple access (CDMA) signals on L3, and, in part, the L1 and L2 frequencies. Depending on the specific satellite platform, either a common antenna or two distinct antennas are used for the individual signals. As a novel feature, the new CDMA navigation messages provide information on the antenna phase center locations as well as the spacecraft orientation during rate-limited noon and midnight turns. We establish a comprehensive set of phase center positions for the individual GLONASS antennas from manufacturer data and information in the CDMA navigation messages, inferred from the comparison of FDMA and CDMA broadcast ephemerides, and obtained from antenna baseline estimates using a triple-frequency carrier phase combination. Based on these, reference values for the signal-specific PCOs of each block of modernized GLONASS spacecraft are derived that may be used to extend the current antenna model of the International GNSS Service (IGS) to all frequency bands. Complementary to the analysis of antenna phase center information, the new CDMA attitude model for GLONASS-K satellites is reviewed. Algorithms for computing relevant parameters of the rate-limited yaw slews including the ramp-up and -down phase based on the Sun/orbit geometry are derived that enable a concise attitude modeling in GLONASS orbit determination and precise point positioning. The model is validated through comparison with yaw angle estimates derived from triple-frequency observations showing consistency at the 1° level for the K2 satellite. On the other hand, unexpected accelerations during noon turns of GLONASS-M+ are identified, which may cause short-term yaw angle deviations of up to 20° relative to attitude models in common use within the IGS.

Real-time passive underwater localization using a compact acoustic sensor array

This paper proposes an underwater passive localization system that can be installed on remote platforms and can locate in real-time sound sources using a compact 4-element array. The localization system relies on both the Time Difference of Arrival (TDoA) algorithm to localize sources that are impulsive, and a low-complexity implementation of the matched field processing (MFP) algorithm for broadband continuous waves, such as propeller noise. The array is intended to be mounted on small platforms and compact geometries are investigated to optimize the localization accuracy. Figures of merit are defined to assess the accuracy and reliability of the algorithm as a function of the distance between the elements. The proposed algorithm is implemented on a System-on-Chip (SoC) which executes the algorithm on an embedded system for real-time operation. Finally, the performance is assessed in realistic ocean conditions in different environments. It is shown that the MFP provides an excellent angle estimate for broadband continuous sources, but TDoA is more reliable, particularly when the deployment is sensitive to the environment conditions.

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources. The outer loop, contingent on the onboard controller and the robot's GPS system, enabling fine adjustments in angle and position relative to the leader's location. The simulation results illustrated the efficiency of the proposed technique in estimating the orientation angles of the leader and the interference sources. The technique robustness is confirmed through testing the performance on different trajectories. Where the follower perfectly generates a main radiation beam directed towards the leader, effectively mitigates interference signals from neighboring group leaders, and successfully tracks the leader path.

Knee Angle Estimation from Surface EMG during Walking Using Attention-Based Deep Recurrent Neural Networks: Feasibility and Initial Demonstration in Cerebral Palsy.

Accurately estimating knee joint angle during walking from surface electromyography (sEMG) signals can enable more natural control of wearable robotics like exoskeletons. However, challenges exist due to variability across individuals and sessions. This study evaluates an attention-based deep recurrent neural network combining gated recurrent units (GRUs) and an attention mechanism (AM) for knee angle estimation. Three experiments were conducted. First, the GRU-AM model was tested on four healthy adolescents, demonstrating improved estimation compared to GRU alone. A sensitivity analysis revealed that the key contributing muscles were the knee flexor and extensors, highlighting the ability of the AM to focus on the most salient inputs. Second, transfer learning was shown by pretraining the model on an open source dataset before additional training and testing on the four adolescents. Third, the model was progressively adapted over three sessions for one child with cerebral palsy (CP). The GRU-AM model demonstrated robust knee angle estimation across participants with healthy participants (mean RMSE 7 degrees) and participants with CP (RMSE 37 degrees). Further, estimation accuracy improved by 14 degrees on average across successive sessions of walking in the child with CP. These results demonstrate the feasibility of using attention-based deep networks for joint angle estimation in adolescents and clinical populations and support their further development for deployment in wearable robotics.

Equilibrium Values for the Si-H Bond Length and Equilibrium Structures of Silyl Iodide and Halosilylenes.

The equilibrium structures of silyl iodide, SiH3I, and silylene halides, SiHX (X = F, Cl, Br, I), were determined by using the mixed regression method, where approximate values of the rotational constants are supplemented by the structural parameters of a different origin. For this goal, it is shown that the r(Si-H) bond length can be determined by using the isolated SiH stretching frequency and that an accurate estimation of the bond angles is obtained by an MP2 calculation with a basis set of triple zeta quality. To check the accuracy of the experimental structures, they were also optimized by means of all electron CCSD(T) calculations using basis sets of quadruple zeta quality.

Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks.

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75-0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

Ambisonics’ setup quality assessment through measurements and computations based on ITD and ILD functions using subbands and a Gammatone Filter Bank

Poznan Supercomputing and Networking Center (PSNC) developed an ambisonic installation and workflow as part of audio-visual 8K VR 360° immersive media experiments. This work aimed to investigate the quality of performance of the PSNC setup through both subjective tests as well as simulations providing objective parameters of interaural characteristics in a real-life scenario of PSNC studio. For the objective part, an algorithm for angle estimation has been proposed and computations were performed.

A geometric solution to microphone array position calibration and its confidence interval analysis

Wireless acoustic sensor networks (WASNs) comprised of spatially distributed acoustic nodes have been increasingly popular in many areas such as speech enhancement, blind source separation, and audio surveillance, where node position information is generally required to implement relevant processing algorithms. In this article, we focus on the node position calibration only using direction-of-arrival (DOA) information with each node consisting of a microphone array. State-of-the-art techniques translate the position calibration into optimization of DOA-based cost functions, and have shown notable success. However, a compromise between position calibration accuracy and computational load still exists. Furthermore, none of the existing studies can offer a quantitative evaluation of calibration performance before the optimization procedure is finished. To address these problems, a geometric solution to array node position calibration is proposed using DOA measurements. For a given array node, DOAs of three sources are first utilized to construct cylinder, plane, and cone equations. Next, the two-dimensional projection of intersections among these equations is calculated using Sylvesterresultantmatrix. Finally, a closed-form solution of node position in three-dimensional space is established. Moreover, explicit mathematical derivation of the confidence interval analysis on the calibration results under given node angle estimation error condition is presented, indicating that the proposed method can provide predictable performance with corresponding probabilities. The Cramér-Rao bound (CRB) is further investigated. Simulation results reveal that as compared with existing methods our approach achieves comparable calibration accuracy with much less computational burden. Real-world experiments also verify its effectiveness.

Smart Drill for a Streamlined Estimation of the Drilling Angle and Channel Length in Orthopedic Surgical Procedures.

The estimation of distances and angles is a routine part of an orthopedic surgical procedure. However, despite their prevalence, these steps are most often performed manually, heavily relying on the surgeon's skill and experience. To address these issues, this study presents a sensor-equipped drill system which enables automatic estimation of the drilling angle and channel length. The angular accuracy and precision of the system were tested over a range of inclination angles and proved to be superior to the manual approach, with mean absolute errors ranging from 1.9 to 4.5 degrees for the manual approach, and from 0.6 to 1.3 degrees with the guided approach. When sensors were used for simultaneous estimation of both the inclination and anteversion angles, the obtained mean absolute errors were 0.35 ± 0.25 and 2 ± 1.33 degrees for the inclination and anteversion angles, respectively. Regarding channel length estimation, using measurements obtained with a Vernier caliper as a reference, the mean absolute error was 0.33 mm and the standard deviation of errors was 0.41 mm. The obtained results indicate a high potential of smart drill systems for improvement of accuracy and precision in orthopedic surgical procedures, enabling better patient clinical outcomes.

Generation of reverse time migration dip gathers with the stabilized Poynting vector and their application in the improvement of subsalt images

Subsalt imaging is challenging with coherent noise prevalent beneath complex salt structures. The coherent noise degrades the imaging quality, making image enhancement and interpretation difficult and potentially erroneous. The dip gathers of reverse time migration (RTM) serve as an ideal domain for separating signals from coherent noise due to their distinct distributions in the dip domain. Methods utilizing the Poynting vector offer an efficient and cost-effective means to produce dip gathers. However, the presence of zero points in the Poynting vector causes instabilities in direction or angle estimation, leading to the leakage of reflection energy into false dip angles. This issue complicates the separation of desired signals from coherent noise. We address this instability by employing a stabilized Poynting vector to produce high-quality RTM dip gathers. The stabilized Poynting vector does not contain zero points within the range of wave propagation, thus mitigating the instability problem. The dip gathers generated using the stabilized Poynting vector provide a clearer and more precise depiction of signal and noise distributions, allowing us to identify the boundaries of desired signals and mute all noise outside these boundaries. Two numerical examples with a synthetic dataset and a field dataset are used to demonstrate our method's effectiveness in reducing coherent noise and improving quality of subsalt images.

Point Ahead Angle(PAA) Estimation and a Control Algorithm for Satellite-Pointing of the Ground Terminal in Satellite-to-Ground Optical Communication

Free-space optical communication technology enables the high-speed data transmission and excellent anti-jamming security. We conduct research on satellite-to-ground free-space optical communication links for high-speed transmission of large-capacity surveillance and reconnaissance data. Since the satellite continues to move along its orbit while the optical signal is transmitted between the satellite and the ground, the pointing angle of the beam from the ground terminal needs to be corrected by Point Ahead Angle(PAA) so that the transmitted light reaches the expected location of the satellite. In this paper, we present the algorithm for PAA estimation and control.