Measurement Vector Research Articles

Ultrasonic wind vector measurement based on quadratic correlation two-frequency phase method

A proposed method for measuring ultrasonic wind speed and direction utilizes the quadratic correlation phase method to address limited range issues and low measurement accuracy associated with the phase difference method when the signal-to-noise ratio is low. First, the proposed method uses quadratic correlation method combined with the four-step phase shift method to estimate the phase difference based on wind measuring structure of the opposing-type Array Structures. Then, the two-frequency phase difference method extends the measurement range of ultrasonic wind speed and direction. Finally, we also built an ultrasonic wind measurement system and perform practical tests, showing that the maximum measurement errors for wind speed and direction angles are within 0.28m/s and 2.8° , which essentially satisffes the ultrasonic anemometer’s design requirements.

Unconstrained measurement method and verification of thrust vector in small solid propulsion system

In response to the inherent limitations of traditional thrust vector measurement methods for small solid rocket motors(SRM), this paper proposes an innovative method based on flight attitude inversion. Flight measurement tests were conducted. Firstly, a six-degree-of-freedom (6-DOF) data inversion algorithm for solving the dynamic data of the three-axis thrust vector was constructed based on the Euler transformation method and the 6-DOF flight motion mathematical model of the flight measurement platform under the action of a three-axis thrust vector. Then, an unconstrained experimental platform was established, obtaining 6-DOF data during the operation of the experimental SRM through an inertial measurement unit(IMU). Finally, the thrust vector data were obtained from the recorded data. During the stable working phase, the average main thrust was 845 N, and the average thrust eccentricity angle was 0.018°, with a main thrust error of 0.6 %, thereby validating the feasibility of the measurement method.

Enhancing robust acoustic DOA estimation against position errors via fast sparse Bayesian learning

Advanced methods for acoustical direction-of-arrival (DOA) estimation leverage sparse signal recovery techniques, which are valued for providing high-resolution results using a limited number of measurement vectors. Although these technologies are capable, they frequently overlook potential errors in sensor positioning, and their computational complexity is excessive, constraining their practical application. This paper introduces a new self-calibrating Bayesian method designed to address this issue. The approach begins by establishing a hierarchical Bayesian model that exploits the sparsity of both signal and position error vectors. Subsequently, a fast algorithm applies a basis selection strategy to efficiently update all variables. Furthermore, the study also explores off-grid estimation. The empirical results indicate that our method outperforms state-of-the-art methods in both accuracy and computational efficiency.

Analysis of the mean electrical axis of the heart from standard electrographic leads in normal adult males

Background: Electrocardiogram has been widely applied as a diagnostic tool for cardiovascular disease. The concept of the cardiac vector is crucial for the clinical application in ECG interpretation. Methods: A prospective cross-sectional study was conducted among 116 male subjects, aged 18-25 years, comprising students and staff of Gandaki Medical College Teaching Hospital and Research Center, Pokhara, Nepal, over the period from June to September 2023. Electrocardiograms (ECGs) were recorded using a standard ECG machine with conventional limb leads. The mean electrical axis of the heart for each individual was determined by plotting the net voltage of the QRS complex in Lead-I and Lead-III. The study aimed to analyze the correlation between the cardiac vector and physical measurements, such as height, weight, Body Surface Area (BSA), and Body Mass Index (BMI). Data analysis was performed using SPSS version 27.0. Results: The normal mean electrical axis of the healthy male subjects was observed as 57.88±24.55°. There was significant positive correlation of cardiac vector with height (p< 0.05), whereas negative correlation was observed with weight and BMI (p< 0.01). However, there was no significant correlation with BSA. In our study we observed the maximum left axis cardiac vector as -10° and right axis as 90° among 116 male subjects. Conclusion: We established a cardiac vector with values differing from those reported in other studies, using standard bipolar limb leads in normal, healthy male subjects. It is also examined that greater BMI has shifted the electrical activity of the ventricles to the left.

Phase-error-free atomic magnetometer and vector measurement method based on demodulated signal phase in geomagnetic environment

The performance of double-beam atomic magnetometer in the geomagnetic environment is greatly affected by the vectorial direction of magnetic field, which leads to the shift of the phase of the demodulation signal. We propose a phase-error-free scalar double-beam atomic magnetometer. When the RF field is along the direction of probe beam, the fluctuation of the phase of the demodulation signal can be avoided. For the practical measurement environment, we propose a phase extraction method for atomic magnetometer. Additionally, based on the phase of the demodulated signal when the RF field is applied in the directions of pumping, probing, and perpendicular to the pumping-detection plane, we propose a method for vector magnetic field measurement. The measurement results indicate that under a 10 μT bias magnetic field, the deviations θ and φ are within 0.1°. The theory and method proposed are of great significance to the application of magnetometer in geomagnetic magnetic field.

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.

Bayesian pathway analysis over brain network mediators for survival data.

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity, and time to disease onset with maximum information extraction, we propose a Bayesian approach to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural model which includes a symmetric matrix-variate accelerated failure time model for disease onset and a symmetric matrix response regression for the network-variate mediator. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Simulations are carried out to confirm the advantages of our proposed method over existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

Improvement in the Number of Velocity Vector Acquisitions Using an In-Picture Tracking Method for 3D3C Rainbow Particle Tracking Velocimetry

Particle image velocimetry and particle tracking velocimetry (PTV) have developed from two-dimensional two-component (2D2C) velocity vector measurements to 3D3C measurements. Rainbow particle tracking velocimetry is a low-cost 3D3C measurement technique adopting a single color camera. However, the vector acquisition rate is not so high. To increase the number of acquired vectors, this paper proposes a high probability and long-term tracking method. First, particles are tracked in a raw picture instead of in three-dimensional space. The tracking is aided by the color information. Second, a particle that temporarily cannot be tracked due to particle overlap is compensated for using the positional information at times before and after. The proposed method is demonstrated for flow under a rotating disk with different particle densities and velocities. The use of the proposed method improves the tracking rate, number of continuous tracking steps, and number of acquired velocity vectors. The method can be applied under the difficult conditions of high particle density (0.004 particles per pixel) and large particle movement (maximum of 60 pix).

Attitude determination for multirotor aerial vehicles using a prescribed‐time super‐twisting algorithm

AbstractThis paper is concerned with the prescribed‐time robust attitude determination (AD) of multirotor aerial vehicles (MAVs) using vector measurements from the local magnetic field and local gravity. To address this problem, we first introduce a novel modified super‐twisting algorithm endowed with the prescribed‐time convergence property. The state of the proposed algorithm is governed by an unbounded time‐varying gain up to the prescribed settling time (PST) and by a function after that. Therefore, after the PST, the new algorithm coincides with the conventional super‐twisting, thus showing robust stability at the origin. This prescribed‐time super‐twisting algorithm (PTSTA) is then applied to the formulation of a three‐stage gyro‐free attitude determination method for MAVs. In the first stage, the classical QUEST algorithm is used to compute a Wahba‐optimal attitude estimate from the vector measurements. In the second stage, the PTSTA is employed in the formulation of a robust state estimator that provides estimates of the attitude Gibbs vector and its rate. Finally, in the third stage, these state estimates as well as the attitude kinematic equation are immediately used to compute the MAV angular velocity. The proposed robust prescribed‐time gyro‐free AD method is evaluated numerically, showing invariance with respect to disturbance and model uncertainty.

An Algorithm for Strapdown Airborne Gravity Disturbance Vector Measurement Based on High-Precision Navigation and EGM2008.

Attitude errors, accelerometer bias, the gravity disturbance vector, and their coupling are the primary factors obstructing strapdown airborne vector gravimetry. This paper takes the geocentric inertial frame as a reference and solves the kinematic equations of its motion and its errors of the body frame and local geographic frame in the Lie group, respectively; the attitude accuracy is improved through a high-precision navigation algorithm. The constant accelerometer bias is estimated through Kalman filtering and is deducted from the accelerometer output to eliminate its influence. Based on the EGM2008 model, the low-frequency components of the gravity disturbance vector are corrected. The gravity disturbance vectors after model data fusion were low-pass filtered to obtain the ultimate results. This method was applied to flight experimental data in the South China Sea, and a gravity anomaly accuracy of better than 0.5 mGal, a northward gravity disturbance accuracy of 0.85 mGal, and an eastward gravity disturbance accuracy of 4.0 mGal were obtained, with a spatial resolution of approximately 4.8 km.

Real-time control action formation for predicting post-accident electrical modes considering permissible stability margins

The object of the research is an emergency control system for ensuring the stability of electric power systems (EPS) in case of emergency unbalances. The relevance of the problem of ensuring EPS stability is due to the need to improve the efficiency of emergency control to reduce the risk of system accidents with significant damage. To solve this problem, we propose an algorithm for selecting the volume of control actions based on the principles of adaptive control for predicting the post-emergency mode with an acceptable stability margin. The algorithm of forming the volume of control actions is based on the dependence of the value of control actions on the value of stability reserves estimation by the value of the Jacobi determinant. To build this dependence, the algorithm of searching for the limiting mode by the trajectory of change in the equilibrium position of the steady state of the system from the initial to the limiting one is used. In contrast to the existing algorithms, the proposed algorithm establishes a functional dependence of the control value on the current parameters of the regime or the stability margin, which increases the efficiency of calculations for selecting control actions. Realization of the proposed algorithm is carried out on the basis of the functional scheme according to the data of the vector measurement system. The advantage and novelty of the proposed algorithm is the possibility of eliminating the disadvantages of existing systems of mode automation, the main of which are: – the necessity to perform numerous variant calculations for selecting the volume of control actions; – possible excessive volume of control actions in case of a mismatch of the actual mode with the calculated one

Maximum-correntropy-based sequential method for fast neural population activity reconstruction in the cortex from incomplete abnormally-disturbed noisy measurements

This paper continues to explore the membrane potential reconstruction and pattern recognition problem in a neural tissue modeled by Stochastic Dynamic Neural Field (SDNF) equation. Although recent research has suggested an efficient solution based on the state-space approach through nonlinear Bayesian filtering framework, it is becoming extremely difficult to ignore the existence of non-Gaussian uncertainties in the SDNFs as well as the stability problem of neuronal population dynamics to outliers. Motivated by recent events in signal processing and mathematical neuroscience, this paper explores the SDNFs in a presence of non-Gaussian uncertainties, which is the shot noise case, where the corrupted data might appear due to broken sensors. We derive the “distributionally robust” state estimator for the membrane potential reconstruction process that is the Maximum Correntropy Criterion Extended Kalman Filter (MCC-EKF) as well as its fast and numerically robust (to roundoff) implementation method by using the sequential principle of processing the measurement vectors. The numerical experiments are provided to illustrate the performance of the novel estimation methods.

Modelling the VaR Using Integral Probability and Tail Dependence

In this paper, a method is proposed to estimate value at risk (VaR) in a multivariate context by employing the probability integral transformation (PIT) and tail dependence function to model extreme dependence structure. The proposed method involves two vectorial measures of VaR, which use either the distribution function or the survival function. Moreover, properties of these risk measures are analysed, and a connection between them and tail dependence functions is established.

An adaptive compressive sensing method on hybrid-field channel estimation for a massive MIMO system

Massive MIMO (Multiple-input-multiple output), crucial for 5 G and Beyond 5 G (B5G) networks, faces challenges with terahertz frequencies in B5G as the communication shifts from far-field to near-field. This shift disrupts traditional Massive MIMO channel estimation, leading to increased pilot overhead and limited performance. The current study used the hybrid-field orthogonal matching pursuit (HF-OMP) algorithm from the compressive sensing (CS) framework to estimate the channel with scatters from both far-field and near-field. The method, however, needs more flexibility regarding scatter location in the field of interest. Also, the usage of HF-OMP under a single measurement vector (SMV) scheme limits the overall performance of the existing method. To address these, we have proposed an adaptive hybrid-field simultaneous-OMP algorithm under multiple measurement vector (MMV) of the CS framework which selects multiple atoms having common support within a single iteration. This innovative approach tackles channel estimation for both far-field and near-field scatters while adapting to their varying distributions. Compared to classical methods, simulations show the new algorithm achieves up to a 14 % improvement in normalized mean square error within a 0 dB to 10 dB signal-to-noise ratio range. This translates to significant reductions in pilot overhead and enhanced channel reliability, paving the way for more efficient and robust wireless networks in the future.

Optimal measurement-based cost gradient estimate for feedback real-time optimization

This work presents a simple and efficient way of estimating the steady-state cost gradient Ju based on available uncertain measurements y. The main motivation is to control Ju to zero in order to minimize the economic cost J. For this purpose, it is shown that the optimal cost gradient estimate for unconstrained operation is simply Jˆu=H(ym−y∗) where H is a constant matrix, ym is the vector of measurements and y∗ is their nominally unconstrained optimal value. The derivation of the optimal H-matrix is based on existing methods for self-optimizing control and therefore the result is exact for a convex quadratic economic cost J with linear constraints and measurements. The optimality holds locally in other cases. For the constrained case, the unconstrained gradient estimate Jˆu should be multiplied by the nullspace of the active constraints and the resulting “reduced gradient” controlled to zero.

Angular velocity observer design using vector measurements with application to attitude tracking

AbstractThis article addresses the design of angular velocity observer for rigid bodies using vector measurements directly. The main contributions of our observer compared with the classical estimation algorithms lie in that it does not need to reconstruct the attitude information from the measurements nor be implemented with velocity sensors. Meanwhile, it provides three‐degrees‐of‐freedom (3DOF) full state estimates. To avoid the unstable critical points for the attitude estimation errors on SO(3), the synergistic potential functions based hybrid method is adopted. Afterwards, an output feedback attitude tracking controller with a proportional‐derivative (PD) plus feedforward form is built based on the estimates from the observer, which can also be implemented using vector measurements directly. Rigorous analysis of the stability of the observer and the combined observer‐controller are presented in detail, with a separation property shown between the observer and the controller. Simulations are conducted to illustrate the effectiveness of the proposed observer and the combined attitude tracking law.

Catalogue of dual-field interferometric binary calibrators

Context. Dual-field interferometric observations with VLTI/GRAVITY sometimes require the use of a binary calibrator. This is a binary star whose individual components remain unresolved by the interferometer, with a separation between 400 and 2000 mas for observations with the Unit Telescopes (UTs), or 1200–3000 mas for the Auxiliary Telescopes (ATs). The separation vector also needs to be predictable to within 10 mas for a proper pointing of the instrument. Aims. No list of properly vetted calibrators was available so far for dual-field observations with VLTI/GRAVITY on the UTs. Our objective is to compile such a list and make it available to the community. Methods. We identified a list of candidates from the Washington Double Star (WDS) catalogue, all with appropriate separations and brightness, scattered over the southern sky. We observed them as part of a dedicated calibration programme, determined whether these objects were true binaries (excluding higher multiplicities resolved interferometrically, but unseen by imaging), and extracted measurements of the separation vectors. We combined these new measurements with those available in the WDS to determine updated orbital parameters for all our vetted calibrators. Results. We compiled a list of 13 vetted binary calibrators for observations with VLTI/GRAVITY on the UTs, and we provide orbital estimates and astrometric predictions for each of them. We show that our list guarantees that there are always two binary calibrators at least at an airmass < 2 in the sky over the Paranal observatory at any point in time. Conclusions. Any principal investigator wishing to use the dual-field mode of VLTI/GRAVITY with the UTs can now refer to this list to select an appropriate calibrator. We encourage the use of whereistheplanet to predict the astrometry of these calibrators, which seamlessly integrates with p2Gravity for VLTI/GRAVITY dual-field observing material preparation.

Method for determining the harmonic contribution of consumer installations based on the application of passive filters

AbstractThe paper presents a new method for estimating the contribution of distortion sources based on the application of passive harmonic filters. The method does not require the measurement of harmonic network impedance and is based on the measurement of harmonic currents of the grid, consumers and passive harmonic filters. Evaluation of the consumer contribution using a passive harmonic filter is necessary to select the parameters and connection points for harmonic reduction devices. A feature of the method is the correct determination of share contributions, regardless of background harmonic distortions. Based on this method, a single consumer can evaluate both the harmonic contributions of its own loads, even in the presence of background distortions, and the harmonic voltage distortions in the case of installing harmonic reduction devices. The research results are confirmed in laboratory conditions with various combinations of electrical loads connected both at the grid side and at the consumer side. For such conditions, the proposed method was compared with existing methods, among which are methods based on the measurement of harmonic voltage vectors, harmonic current vectors and active harmonic power. The application of the developed method was demonstrated using the example of a gas production field.

Pengaruh Sudut Terhadap Besar Resultan Gaya Vektor: Kajian Eksperimental Menggunakan Praktikum Cookbook Laboratory

Vectors are fundamental concepts in mathematics and physics that play a crucial role in various scientific and engineering applications. This study investigates the relationship between the angle and magnitude of the resultant force vector using the Cookbook Laboratory practical approach. The experiment involved combining two forces at different angles and measuring the resultant force. The results indicate a linear relationship between the angle and magnitude of the resultant force, suggesting that as the angle increases, the magnitude of the resultant force also increases. However, this finding contradicts the theoretical prediction of an inverse relationship. This discrepancy is attributed to experimental limitations, particularly parallax error, which may have affected the accuracy of the measurements. Further experiments are recommended to minimize parallax error and obtain more accurate results. A thorough understanding of vector concepts and measurement techniques is essential for comprehending the relationship between angle and resultant force magnitude. Keywords: Angle; Cookbook Laboratory; Resultant Force; Vector.