Angle Estimation Accuracy Research Articles

Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method.

Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs’ reference frames, referring to IMUs’ individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames’ deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.

Standardization of penile angle estimation with a semi-automated algorithm

Penile curvature (PC) refers to an abnormal bending of the main longitudinal axis of the penis. It is frequently associated to hypospadias. To date, accurate and objective evaluation of PC is not easily reproducible amongst surgeons and there are no stablished protocols on how to measure PC in a standard way and in real-time to guide intraoperative decision making. For this reason, we want to present the results of creating a semi-automated algorithm to establish a reproducible and objective assessment of PC and propose it as a standard protocol for clinical applicability using inanimate 3-D penile models. This project consisted in two different phases. 1. Creation of an automated algorithm to estimate penile angle based on digital images. 2 Use of the algorithm to estimate penile angle on 3-D models and estimate interrater agreement using the algorithm. The algorithm was created to initially identify the geometrical centerline of the penile model to establish an automated output for angle estimation. 3-D printed penile models with known curvature angles ranging from 10 to 90° were used to test the algorithm (total of 9 penile models. These models were curved at one hinge as opposed to an arc type model. For each inanimate model, a set of 5 pictures were obtained from a lateral view at different camera angles (00, 150, 300, 450 and 600) at a standard distance of 75cm. Angle estimation using our designed PC algorithm was performed by a total of 10 different evaluators. Inter-rater reliability analysis in using the semiautomated algorithm was performed using the inter-class correlation coefficient (ICC) with two-way mixed effect model. If the camera angle was greater than 30°, the absolute angle mean difference was greater than 10°. Camera angle with the smallest mean difference was at 00 with a mean difference of 7.83°. Agreement between raters showed greater variability towards the higher camera angles. Nonetheless, a high degree of between evaluator reliability was found between the measurements at different camera angles. Single measures ICC ranges from .873 to .946, p-values were all <.0001. Our results help standardize PC assessment using digital images and reduce subjectivity using an algorithm for PC estimation. Optimal camera position between 00 to 300 vertical from the penis gives the least variable and most accurate angle estimation. Future studies using algorithms will help define predictive PC cutoff values and evaluate postoperative outcomes.

Square-shaped sensor clusters for acoustic source localization in anisotropic plates by wave front shape-based approach

Various techniques have emerged in the past few years for localizing the acoustic source in an anisotropic plate. The wave front shape-based approach, one of the recent additions to this field of research, has the advantage of circumventing the unrealistic assumption of a straight line wave propagation path through an anisotropic medium. In their most recent versions, the two major wave front shape-based techniques (i.e., the ellipse- and the parametric curve-based techniques) are applicable to the situations with an unknown orientation of the axes of symmetry of an anisotropic plate. However, this approach still relies on estimating the angle of wave-incidence at a given location of the plate via a single L-shaped sensor cluster. The incidence angle so obtained may deviate significantly from the true angle of wave arrival. To improve the estimation accuracy of the incidence angle, in the present study a square-shaped cluster composed of four densely-spaced sensors forming the four vertices of a square is proposed to be installed at the location of interest. Essentially, a square-shaped sensor cluster contains four L-shaped clusters oriented in different directions. A formulation to estimate the angle of incidence from the signal data acquired by a squared-shaped cluster is presented. The wave front shape-based approach can then be applied to estimate the acoustic source location. A numerical study is conducted to illustrate the proposed methodology. Performance comparisons between square- and L-shaped clusters reveal that in general the square-shaped clusters lead to more accurate source location estimates than the L-shaped clusters.

Adaptive Polarization-Constrained Monopulse Approach for Dual-Polarization Array

In this letter, the problem of monopulse angle estimation error under mainlobe interference for dual- polarization array is investigated. The adaptive monopulse angle estimation considering cross polarization with array beam scanning is analyzed. To reduce the angle estimation error, an adaptive polarization-constrained monopulse approach based on combined constrained cross polarization and main polarization is proposed. The cross polarization of antenna and target signal is not regarded as a source of estimation error which needs to be suppressed in previous literature. Instead, it contributes to the angle estimation here. The proposed method could obtain accurate monopulse angle estimation while suppressing mainlobe interference. Simulation results verify the performance of the proposed method.

A Novel Method for Lower Limb Joint Angle Estimation Based on sEMG Signal

Due to the strong dynamic and time-varying characteristics of the surface electromyography (sEMG) signal and the coupling between lower limb muscles, it is challenging to accurately estimate the joint angle of complex lower limb motion. Firstly, we proposed a wavelet packet decomposition correlation dimension (WPD_CD) analysis method. A three-level wavelet packet decomposes the sEMG signal, and the optimal and stable feature vector is extracted by the correlation dimension analysis. A singular spectrum nonlinear autoregressive exogenous model (NARX_SSA) is proposed to map the optimal eigenvector to the lower limb joint angle to improve the angle accuracy and smoothness. The experimental results show that this method only needs to collect three lower limb muscles and accurately estimate four movements of knee and hip angles. The overall RMSE was 0.32, the minimum RMSE was 0.20, and the standard deviation (SD) was 20.23. Compared with other methods, the accuracy of angle estimation was improved by six times, and the smoothness was improved by 13%. Therefore, this method can be effectively applied to the angle estimation of complex lower limb motion patterns.

Rotation-Aware 3D Vehicle Detection From Point Cloud

Three-dimensional vehicle detection using LiDAR point clouds is important for the stability of autonomous driving. It can provide high-quality three-dimensional information for most obstacles. Although efficient algorithms based on a Bird’s Eye View (BEV) feature map have been developed, many research issues still remain; in particular, the existing methods show a limited accuracy in estimating the rotation angle of a 3D object. In this paper, to improve the accuracy of rotation angle estimation, we propose a rotation-aware 3D vehicle detector that extracts distinguishable features from the proposals with various angles of rotations. Experiments are conducted on KITTI dataset and Waymo Open dataset. Our approach improves detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency.

Dual-Cross-Polarized GPR Measurement Method for Detection and Orientation Estimation of Shallowly Buried Elongated Object

Detecting a shallowly buried and elongated object and estimating its orientation using a commonly adopted co-polarized GPR system is challenging due to the presence of strong ground clutter that masks the target reflection. A cross-polarized configuration can be used to suppress ground clutter and reveal the object reflection, but it suffers from inconsistent detection capability which significantly varies with different object orientations. To address this issue, we propose a dual-cross-polarized detection (DCPD) method which utilizes two cross-polarized antennas with a special arrangement to detect the object. The signals reflected by the object and collected by the two antennas are combined in a rotationally invariant manner to ensure both effective ground clutter suppression and consistent detection irrespective of the object orientation. In addition, we present a dual-cross-polarized orientation estimation (DCPOE) algorithm to estimate the object orientation from the two cross-polarized data. The proposed DCPOE algorithm is less affected by environmental noise and performs robust and accurate azimuth angle estimation. The effectiveness of the proposed techniques in the detection and orientation estimation and their advantages over the existing method have been demonstrated using experimental data. Comparison results show that the maximum and average errors are 22.3{\deg} and 10.9{\deg} for the Alford rotation algorithm, while those are 4.9{\deg} and 1.8{\deg} for the proposed DCPOE algorithm in the demonstrated shallowly buried object cases. The proposed techniques can be unified in a framework to facilitate the investigation and mapping of shallowly buried and elongated targets.

FDA-MIMO Radar With Long-Baseline Transmit Array Using ESPRIT

The frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar combined with long-baseline transmit array (LBTA-FDA-MIMO) is presented for high accuracy direction-of-arrival (DOA) estimation, and the transmit array is designed in the form of a four-antenna interferometer in this letter. However, the ambiguities of both angle and range exist at the same time. To deal with this problem, a joint-disambiguation method based on ESPRIT is proposed and the uniqueness theorem is proved. The main idea of this method is that the unambiguous-rough-estimation for angle is used to solve the ambiguities of fine-estimation expressions coupled with angle and range. Compared with FDA-MIMO radar, the LBTA-FDA-MIMO radar significantly improves the angle estimation accuracy, and interestingly, it also improves the ranging accuracy via simulations.

Accurate and objective determination of myalgic encephalomyelitis/chronic fatigue syndrome disease severity with a wearable sensor

BackgroundApproximately 2.5 million people in the U.S. suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This disease negatively impacts patients’ ability to function, often resulting in difficulty maintaining employment, sustaining financial independence, engaging socially with others, and in particularly severe cases, consistently and adequately performing activities of daily living. The focus of this research was to develop a sensor-based method to measure upright activity defined as time with feet on the floor and referred to as UpTime, as an indicator of ME/CFS disease severity.MethodsA commercially available inertial measurement unit (IMU), the Shimmer, was selected for this research. A Kalman filter was used to convert IMU data collected by the Shimmer to angle estimates. Angle estimate accuracy was confirmed by comparison to a motion capture system. Leg angle estimates were then converted to personalized daily UpTime scores using a critical angle of 39º from vertical to differentiate between upright (feet on the floor) and not upright. A 6-day, case–control study with 15 subjects (five healthy controls, five moderate-level ME/CFS, and five severe-level ME/CFS) was conducted to determine the utility of UpTime for assessing disease severity.ResultsUpTime was found to be a significant measure of ME/CFS disease severity. Severely ill ME/CFS patients spend less than 20% of each day with feet on the floor. Moderately ill ME/CFS patients spend between 20–30% of each day with feet on the floor. Healthy controls have greater than 30% UpTime. IMU-measured UpTime was more precise than self-reported hours of upright activity which were over-estimated by patients.ConclusionsUpTime is an accurate and objective measure of upright activity, a measure that can be used to assess disease severity in ME/CFS patients. Due to its ability to accurately monitor upright activity, UpTime can also be used as a reliable endpoint for evaluating ME/CFS treatment efficacy. Future studies with larger samples and extended data collection periods are required to fully confirm the use of UpTime as a measure of disease severity in ME/CFS. With the added perspective of large-scale studies, this sensor-based platform could provide a recovery path for individuals struggling with ME/CFS.

Design of a broadband dielectric resonator antenna and its application in low sensitivity frequency beam‐scanning array with wide scanning angle and suppressed sidelobes

In this paper, a broadband dielectric resonator antenna (DRA) with a simple H-slot feeding structure is proposed. The broad bandwidth (~45%) and uni-directional radiation enable this DRA suitable to work as a radiation element in the construction of a beam-scanning array with not only low frequency sensitivity which is important for accurate angle estimation of in-coming targets, but also a wide scanning range. Moreover, stable coupling coefficients are established around all the above three resonances with respect to its corresponding feeding line, ensuring a low sidelobe level (SLL) across the whole operating frequency range. In order to realize such a DRA, a latticed rectangular structure is utilized to generate the lower two resonances, while the upper resonance is generated by its H-shaped feeding slot. The shape of the latticed dielectric resonator is also modified according to a 3-D printed fixing structure for an accurate installation. A 20-element beam-scanning prototype is demonstrated and manufactured. The range of scanning angle is from −36° to +13° within the bandwidth of 10 to 16 GHz, corresponding to a frequency sensitivity of only 122.5 MHz/o. The achieved SLL are smaller than 19 dB for all scanning beams. The above performance indicates that, this array is very suitable for near-range radar systems requiring an accurate angle estimation.

IMU-Based Automated Vehicle Body Sideslip Angle and Attitude Estimation Aided by GNSS Using Parallel Adaptive Kalman Filters

The sideslip angle and attitude are crucial for automated driving especially for chassis integrated control and environmental perception. In this article an inertial measurement unit (IMU)-based automated vehicle body sideslip angle and attitude estimation method aided by low-sample-rate global navigation satellite system (GNSS) velocity and position measurements using parallel adaptive Kalman filters is proposed. This method can estimate the sideslip angle and attitude simultaneously and is robust against the vehicle parameters and road friction even as the vehicle enters critical maneuvers. First, based on the acceleration and angular rate from the six-dimensional inertial measurement unit, the attitude, velocity and position (AVP) are integrated with the navigation coordinates and the AVP error dynamics and observation equations of the integration results are developed. Second, parallel innovation adaptive estimation (IAE)-based Kalman filters is designed to estimate the AVP error of the integration method to address the issues of the GNSS low sampled rate and abnormal measurements. Then the AVP error is forwarded to the AVP integration to compensate the accumulated error. To improve the heading angle estimation accuracy, the heading error is estimated by a decoupled IAE-based Kalman filter aided by GNSS heading. In addition, time synchronization of the IMU and GNSS is realized through hardware based on the pulse per second signal of the GNSS receiver and the spatial synchronization is achieved by a direct compensation method. Lastly, the sideslip angle and attitude estimation method is validated by a comprehensive experimental test including critical double lane change and slalom maneuvers. The results show that the estimation error of the longitudinal velocity and lateral velocity is smaller than 0.1 m/s $({1\sigma })$ , and the estimation error of the sideslip angle is smaller than 0.15° $({1\sigma })$ .

Vehicle sideslip angle estimation by fusing inertial measurement unit and global navigation satellite system with heading alignment

Estimation of the sideslip angle is significant for vehicle safety control systems such as electronic stability control. This paper proposes a vehicle-kinematic-model-based sideslip angle estimation method by fusing the information from an inertial measurement unit (IMU) and global navigation satellite system (GNSS) with aligning the heading from the GNSS. To estimate the velocity and attitude errors of the reduced inertial navigation system (R-INS), we first formulate the associated system error dynamics. Then, to further improve the heading estimation accuracy of the R-INS, the heading from the GNSS is aligned to the vehicle longitudinal direction by a robust regression method and adopted to estimate the heading error of the R-INS. Next, an adaptive Kalman filter is applied to estimate the errors in the R-INS to attenuate the noise influence. With the velocity in navigation coordinates and the attitude between the navigation coordinates and vehicle body coordinates from the R-INS, the velocity and sideslip angle in the vehicle body coordinates are computed. Finally, tests in straight line, double lane change (DLC), and slalom maneuvers are performed to verify the sideslip angle estimation and the heading alignment method. After aligning the heading from the GNSS, the sideslip angle estimation accuracy is improved, and the mean error under typical DLC and slalom maneuvers are below 0.21°.

Angle of impact determination from bullet holes in a metal surface

Using the best-fit ellipse method, single bullet impacts in thin sheet metal were assessed to investigate the accuracy of impact angle estimation. When a bullet passes through a metal panel, the yielding nature of metal causes changes to the metal surface and the resultant hole. This deformation of the metal complicates the assessment of single impacts using the ellipse method. Determining the correct impact angle may not be obvious and results in considerable errors between the known and calculated angle. To determine if the calculated angle varies in any particular way to the known angle, impacts were created on metal panels using six different types of 9 mm ammunition and seven angles from 90° to 14°. Impact angles, determined using the ellipse method, were compared with known firing angles and the error pattern assessed. The results show an error pattern with a significant quadratic relationship for three ammunition types, with the error pattern for the remaining three ammunitions not explained by a quadratic formula and requiring further study. Results suggest that single bullet impacts for a given type of ammunition with a quadratic error pattern, can be assessed with accuracy due to a more consistent behavior. This characteristic pattern of error requires further study but is a promising step for determining an accurate impact angle and bullet path from a single impact point in a metal surface.

Vision‐aided intelligent vehicle sideslip angle estimation based on a dynamic model

The vehicle sideslip angle is an important state for vehicle dynamic control, which needs to be estimated as it could not be obtained directly by the vehicle. To improve the estimation accuracy of the sideslip angle based on the intelligent vehicle platform, this study proposes a novel vehicle sideslip angle estimation algorithm with the fusion of dynamic model and vision information. Firstly, to further improve the model accuracy of the vehicle during lateral acceleration conditions, a vehicle dynamic model is established considering the acceleration error compensation with the assistance of attitude information. In addition, based on the lane line information obtained from the equipped camera in intelligent vehicles, a visual geometric model is established. Owing to the measurement delay and low sampling frequency of the camera, a multi-rate sideslip angle observer with delay compensation is designed to coordinate with the inter-frequency signal of the vehicle chassis. Finally, the effectiveness of the algorithm is verified by the slalom test.

Accurate Angular Inference for 802.11ad Devices Using Beam-Specific Measurements

Due to their sparsity, 60GHz channels are characterized by a few dominant paths. Knowing the angular information of their dominant paths, we can develop various applications, such as the prediction of link performance and the tracking of 802.11ad devices. Although they are equipped with phased arrays, the angular inference for 802.11ad devices is still challenging due to their limited number of RF chains and limited phase control capabilities. Considering the beam sweeping operation and the high communication bandwidth of 802.11ad devices, we propose variation-based angle estimation (VAE), called VAE-CIR, by utilizing beam-specific channel impulse responses (CIRs) measured under different beams and the directional gains of the corresponding beams to infer the angular information of dominant paths. Unlike state-of-the-arts, VAE-CIR exploits the variations between different beam-specific CIRs for angular inference and provides a performance guarantee in the high signal-to-noise-ratio region. To evaluate VAE-CIR, we generate the beam-specific CIRs by simulating the beam sweeping of 802.11ad devices with the beam patterns measured on off-the-shelf 802.11ad devices. The 60GHz channel is generated via a ray-tracing-based simulator and the CIRs are extracted via channel estimation based on Golay sequences. Through extensive experiments, VAE-CIR is shown to achieve more accurate angle estimation than existing schemes.

Sparse nested array with aperture extension for high accuracy angle estimation

Sparse nested arrays (SNAs) are presented for highly accurate angle estimation. A nested array is sparse if its sensor-spacing of the first level is extended beyond a half-wavelength. Each array grid is equipped with an identical two-component subarray, such as a half-wavelength spaced doublet, a spatially-colocated p-u probe and a spatially-orthogonal dipole/loop pair, to solve angle ambiguities caused by aperture extension.

Алгоритмы и конфигурация системы ориентации подвижных объектов на микромеханических чувствительных элементах

Inertial systems for attitude control, stabilisation and navigation of moving objects boast a range of unique qualities, the most important of which are autonomy and interference immunity. At present, strap-down inertial navigation systems using inexpensive and compact microelectromechanical sensors are popular. The biggest disadvantage of the attitude control systems utilising microelectromechanical sensors is rapid error accumulation over time. The main error sources in strap-down inertial navigation systems are the errors of angular velocity sensors and accelerometers. Currently the accuracy required is ensured by the attitude control system processing the following two signals simultaneously: the magnetometer signal and the signal received from global navigation satellite systems such as GPS (NAVSTAR) and/or GLONASS. We developed an unconventional approach to integrating the two systems, that is, a strap-down inertial navigation system and a global navigation satellite system. It involves using the difference between the accelerations computed according to the global navigation satellite systems and those computed by the acelerometers and transformed to the geographic coordinate system for evaluating and compensating for the error of attitude angle assessment via the kinematic channel. Since this approach does not use integration of accelerometer readings, the attitude angle errors at the initial stage do not accumulate over time. Numerical simulation results of the algorithms developed show that the attainable attitude angle estimation accuracy significantly exceeds the accuracy of conventional methods

Research on Monopulse Angle Measurement Algorithm in Pseudolite Local Positioning System

The pseudo-satellite local positioning system can solve the problem of blind spots in satellite navigation systems, such as tunnels, underground parking lots, and indoor positioning. The method of direction-finding positioning can be combined with distance-finding positioning to improve positioning accuracy, and it can also independently realize positioning functions. The basis of direction-finding positioning is to accurately estimate the direction of the signal source. The sum-difference patterns of amplitude monopulse angle measurement algorithm has mature applications in the field of tracking radar, and can achieve accurate angle estimation. The sum-difference patterns of amplitude monopulse angle measurement algorithm is applied to a pseudo-satellite local positioning system. Through simulation experiments, the angle measurement performance of the algorithm under different conditions is analyzed.

Continuous angular position estimation of human ankle during unconstrained locomotion

ObjectiveActive lower limb prostheses required an efficient user interface and robust control structure for its seamless operation. A nonlinear autoregressive model has been proposed and evaluated to estimate the ankle joint angle continuously. It utilizes surface electromyogram (SEMG) and knee joint angle (KA) signals inputs. MethodThe performance of the proposed model has been assessed based on the accuracy and consistency of angle estimation. A dataset of ten subjects acquired for five daily life locomotor activities has been used for model performance evaluation. Also, the contribution of KA signal towards ankle joint angle estimation has been examined. ResultsThe average angle estimation error over the subjects has been found in the range of 2.38 ± 0.78° to 5.45 ± 1.98° for various dynamic activities. The contribution of KA signal has been found significant (One-way ANOVA, p-value<0.05) as it improves the model accuracy and consistency by 52% and 36%, respectively. SignificanceThe proposed model provides an opportunity for direct control of ankle-foot prostheses by continuously predicting the ankle joint angle using SEMG and KA signal. The model’s performance proves its applicability for ankle joint angular orientation estimation for active prostheses, orthoses, and lower limb rehabilitation.

Ultrawideband Discharge Source DOA Estimation Method Using Multiple Baseline Wideband Time-Domain Interferometry with Hilbert Transform

The research interest of ultrawideband (UWB) discharge source location estimation has increased these years. In this paper, a direction of arrival (DOA) estimation method using multiple baseline wideband time-domain interferometry with Hilbert transform for UWB discharge source is proposed based on time-domain and frequency-domain characteristics of radiated RF electromagnetic pulses (EMPs) from discharge sources. Monte Carlo simulations are then carried out; the results indicate that, the proposed method provides a better performance in UWB discharge source DOA estimation than the traditional time-domain method, especially in low signal-to-noise ratio (SNR) conditions. Moreover, the influences of antenna array configurations and incident angles of radiated EMPs on the estimation precision are also studied. It has been shown that, the accuracy of both elevation angle and azimuth angle estimation improves with the increase of the antenna element number and baseline length. As for the influence of incident angles, the estimation accuracy of elevation angle enhances when real elevation angle increases, while that of azimuth angle tends to be opposite. Meanwhile, the real azimuth angle has little effect on the DOA estimation. Finally, an experimental setup for discharge source DOA estimation is introduced and the experiment results are illustrated.