Measurement Vector Research Articles

Assessment of magnetic field on body axis and orbit axis of RazakSAT in Near Equatorial Orbit using IGRF model

Magnetometer is reference sensor of satellite that function to measure the vector of magnetic field during satellite enter the orbit. The magnetic field measurement vector will be employed in the Attitude Determination System (ADS) to calculate the satellite’s orientation with respect to the Earth. However, any misalignments or disturbances on the ADS can affect the orientation of the satellite in terms of the orbital position. Therefore, in this paper assessment of magnetic field on body axis and orbit axis of RazakSAT by using The International Geomagnetic Reference Field (IGRF) model. RazakSAT as references to verify the IGRF model by using residual analysis that percentage error less than 5% requirement from Astronautic Technology Sdn Bhd (ATSB). The result show, Y-axis and Z-axis from orbit frame meet the requirement of ATSB is less than 5%.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

Exploring the Spectrum of Analytical Techniques for Material Characterization

Imaging spectroscopy integrates the spatial information of a measured sample with its chemical or physical information, enabling comprehensive material characterization and analysis. This tutorial delves into the intricacies of various imaging spectroscopy methods, which apply to multiple spectroscopic regions. Imaging spectroscopy may be accomplished for ultraviolet-visible (UV-vis), fluorescence (FL), near-infrared (NIR), infrared (IR), terahertz (THz), Raman, X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS), or laser-induced breakdown spectroscopy (LIBS). Measurement data from a single spectral region or multiple spectral regions may be used. Measured spectral data may be analyzed separately or fused into a continuous measurement vector (spectrum) before information processing. For the sampled area, spectral data may be collected from a point, small region, or wide-field. The principles, applications, advantages, and limitations of spectroscopic imaging are discussed here, enabling researchers to have a more comprehensive understanding of the use of spectroscopic imaging for measuring a variety of sample types.

MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography.

Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This article presents a multiple measurement vector (MMV) model-based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The nonlinear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intrafrequency and interfrequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness, and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising

In wireless multipath channels, there is sparsity, in which traditional channel estimation algorithms do not take advantage of and ignore the impact of noise. An effective estimation algorithm combining compressed sensing and wavelet de-noising is proposed. Before channel estimation, the pilot signal received by the receiver is de-noised by the wavelet de-noising method to obtain the de-noised measurement vector, and then the original signal is recovered by the compressed sensing technology for sparse channel estimation. This algorithm breaks away from the limitation of traditional algorithms that require predictive sparsity to achieve sparsity adaptive signal reconstruction. Through simulation analysis, it has been proven that the fusion wavelet denoising algorithm can improve the estimation performance, reduce estimation errors, and be more suitable for sparse channel estimation by denoising the signal during channel estimation.

Influence of aircraft attitude on fully polarized radiometer and its correction

ABSTRACT As an important load of airborne/spaceborne passive microwave remote sensing, fully polarized microwave radiometer is widely used to the measurement of wind vector and salinity on sea surface. The retrieval of ocean surface parameters requires high accuracy data of radiometer. In recent years, the data correction scheme of fully polarized radiometer is common, but the attitude problem of radiometer is not paid enough attention. This paper establishes a full link transfer model for a fully polarized microwave radiometer, and focus on analysing the impact of aircraft attitude on various parameters. This paper aims to draw researchers’ attention to attitude control of fully polarized microwave radiometer. Through the model established in this paper, the influence of attitude on fully polarized microwave radiometers is clarified and corrected, then rapidly correction can be achieved. Through simulation model, the impact of different attitudes on data can be calculated in advance before flight tests, and can provide guidance for attitude control. Using the corrected data for wind vector analysis, the deviation of wind direction is within 10 ∘ , which also verifies the accuracy of the model in this paper. The model proposed in this paper can achieve fast and accurate correction of radiometer parameters and the error prediction caused by attitude, which provides a strong guarantee for the inversion of sea surface parameters.

Development and evaluation of laser acoustic measurement system for supersonic jet noise

Acoustic measurements in the near field are significant to suppress the noise generated by a supersonic jet. The microphone measurement is commonly used to measure noise, but it is not appropriate for the jet noise measurement in the near field, because it may be broken or disturb the sound field. In this study, we propose an optical acoustic measurement method using a laser and a 2-D position sensitive detector. This system can measure the propagating direction and the spectra of acoustic waves by detecting the angle and direction of laser path deflection by acoustic wave passing. To evaluate this laser measurement method, it was applied to various supersonic jet noise phenomena, such as Mach waves and screech tones, and then the results were compared with those of microphone measurements. The spectra measured by the laser measurement show good agreement with those by microphone measurements, and the difference in OASPL was less than 0.7 dB. As for the propagating direction, it is calculated using the principal component analysis in the laser measurement, and its results also show good agreement with those of acoustic intensity vector measurement.

Selected aspects of measurement data processing in electronic warfare devices

Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI

Multi-day activity pattern recognition based on semantic embeddings of activity chains

Understanding residents' multi-day activity patterns is crucial for urban transportation planning. However, traditional travel surveys have limitations in observing the recurring regularity of residents' activities over multiple days, resulting in limited insights into long-term activity patterns. This study aims to provide a general and practical analytic framework for multi-day activity pattern recognition by fusing mobile phone data, travel survey data, and built environment data. First, a rule-based model is proposed to infer home/work activities, while a machine learning model is proposed to infer non-home/work activities of mobile phone users. Then, their multi-day activity chains are reconstructed with sequences of activity units which are labeled by inferred activity types. To measure the differences and similarities among these activity chains, this study develops a word embedding model to represent activity units and a sentence embedding model to represent activity chains in numerical vector space. Finally, regular multi-day activity patterns of mobile phone users can be recognized based on the clustering of vectorized activity chains. A case study in Shanghai is conducted to validate the proposed analytic framework for understanding the multi-day activity patterns of residents. The results indicate the effective recognition of 39 representative patterns of 7-day activity chains in Shanghai, reflecting the various lifestyles of residents. In summary, the main contribution of this study is to transform the problem of multi-day activity pattern recognition into similarity measurement of sentence vectors of activity chains by employing techniques from the field of natural language processing.

Effect of Magnetic Shunts on Shell-Type Transformers Characteristics

A three-dimensional analysis of the leakage magnetic field in a transformer with beveled edges in a magnetic shunt is described. This paper contains a mathematical description of the finite element method used in performing numerical calculations. Formulas allowing to determine the differential and integral parameters of the magnetic field were presented. Magnetic flux density distributions and the points with high values were determined in the shell-type core and movable shunt sub-areas. For different positions of the magnetic shunt, the short-circuit transformer reactance was calculated as the integral parameter of the above-mentioned magnetic field analysis. Their amounts for the extreme positions of the shunt were given, as well. The movable magnetic shunt geometry impact on the short-circuit current value is given. In order to validate the calculation results, the research included some point measurements of magnetic flux density vectors and the winding reactances as well, and good compliance with the simulation data has been obtained. Thus, this analysis can also be applied to other objects with movable shunts in their magnetic systems.

Controlled Polarized Optical Excitation Method on Ensembles of Nitrogen‐Vacancy Centers in Diamonds

AbstractCrystal axes must be distinguished in vector magnetic measurements of ensemble nitrogen‐vacancy (NV) centers in diamonds. However, achieving crystal axes distinction in confocal systems without a magnetic bias field is challenging. Thus, a controlled polarized optical excitation method to overcome this problem is proposed. The polarization angle of the excitation beam can be adjusted in free space using an optical pen for higher controllable degrees of freedom. Compared to previous optical excitation methods, the proposed method improves the maximum suppression by 19.2% and the maximum excitation by 42.4%. Moreover, the proposed method has a decoupling effect on NV axis resonances with a similar change trend. This method can easily replace existing optical excitation methods in NV measurement systems and is a powerful tool for ensembles of NV centers’ vector measurements without relying on magnetic bias fields.

Reflection-based vision guide method for coarse alignment of vector measurement during the satellite assembly, integration, and test process.

Vector measurement is a vital measurement item during the satellite assembly, integration, and test (AIT) process. With the increasing popularity of commercial spaceflight, the development cycle of a satellite is shorter, and the number of satellites has been growing rapidly. The traditional on-site vector measurement method is inefficient and significantly affects the development cycle of the satellite. Therefore, it is of utter importance to propose an online high-precision automatic vector measurement system. The most challenging step of the online automatic vector measurement is coarse alignment because a cubic prism must be identified, and the normal direction of its surface must be calculated at a certain precision in the unstructured environment during the coarse alignment step. A reflection-based vision guide method was proposed to identify and calculate the normal direction of the cubic prism. The working principle and advantage of the proposed vision guide system were described in detail. What is more, the calibration and calculation methods of the proposed vision guide system were also presented. Finally, experiments were conducted to verify the effectiveness of the proposed method.

RDLNet: A Regularized Descriptor Learning Network.

Local image descriptor learning has been instrumental in various computer vision tasks. Recent innovations lie with similarity measurement of descriptor vectors with metric learning for randomly selected Siamese or triplet patches. Local image descriptor learning focuses more on hard samples since easy samples do not contribute much to optimization. However, few studies focus on hard samples of image patches from the perspective of loss functions and design appropriate learning algorithms to obtain a more compact descriptor representation. This article proposes a regularized descriptor learning network (RDLNet) that makes the network focus on the learning of hard samples and compact descriptor with triplet networks. A novel hard sample mining strategy is designed to select the hardest negative samples in mini-batch. Then batch margin loss concerned with hard samples is adopted to optimize the distance of extreme cases. Finally, for a more stable network and preventing network collapsing, orthogonal regularization is designed to constrain convolutional kernels and obtain rich deep features. RDLNet provides a compact discriminative low-dimensional representation and can be embedded in other pipelines easily. This article gives extensive experimental results for large benchmarks in multiple scenarios and generalization in matching applications with significant improvements.

Sparse Recovery of Finite Alphabet Signals Using AMP and Its Application in DoA Estimation

This study presents an approximate message passing (AMP)-based method for the reconstruction of sparse signals from multiple measurement vectors. Recently, Ziniel and Schniter have provided a general analysis of the AMP algorithm. They have assumed that the nonzero elements of the sparse signals have Gaussian distribution. This article extends their proposed method to the case that the nonzero elements of sparse vectors are chosen from a discrete finite alphabet. Therefore, the proposed algorithm is called approximate message passing with finite alphabet. Based on this assumption, the passing messages have been derived and the scheduling of the algorithm has been proposed using a three-step strategy. As an application, direction of arrival (DoA) estimation of digitally modulated signals is examined using simulations. The obtained results revealed that the proposed method would improve the DoA estimation accuracy compared to the conventional methods.

Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty

The solution of probabilistic inverse problems for which the corresponding forward problem is constrained by physical principles is challenging. This is especially true if the dimension of the inferred vector is large and the prior information about it is in the form of a collection of samples. In this work, a novel deep learning based approach is developed and applied to solving these types of problems. The approach utilizes samples of the inferred vector drawn from the prior distribution and a physics-based forward model to generate training data for a conditional Wasserstein generative adversarial network (cWGAN). The cWGAN learns the probability distribution for the inferred vector conditioned on the measurement and produces samples from this distribution. The cWGAN developed in this work differs from earlier versions in that its critic is required to be 1-Lipschitz with respect to both the inferred and the measurement vectors and not just the former. This leads to a loss term with the full (and not partial) gradient penalty. It is shown that this rather simple change leads to a stronger notion of convergence for the conditional density learned by the cWGAN and a more robust and accurate sampling strategy. Through numerical examples it is shown that this change also translates to better accuracy when solving inverse problems. The numerical examples considered include illustrative problems where the true distribution and/or statistics are known, and a more complex inverse problem motivated by applications in biomechanics.

Ultrasonic Velocity Profiler for the Measurement of a Bubbly Flow Velocity Vector in Small Channels

The multi-dimensional velocity distribution of coolant in bubbly flow within the fuel rod bundles of the reactor core in boiling water reactors (BWRs) is elucidated by experimental investigationin this study. Since a measurement technique is required for such an investigation, this paper proposes the development of an ultrasonic velocity profiler (UVP). The combination of special ultrasonic transducers and modified signal processing on the UVP is proposed to obtain a multi-dimensional velocity vector of the bubbles and liquid in bubbly flow. The ability of the proposed technique is demonstrated by performing an experiment in swirling bubbly flowand itsapplicability confirmed by comparing the resultswith another technique. The sound pressure distribution in the narrow channel of the rod bundle isthenmeasuredprior to the verification ofthe ultrasonic wave emitted through a small channel. The echo signal reflected from reflectors dispersed in the liquid,bubble, and tracer particles in the small channel of the rod bundle indicates that the proposed UVP can be applied inthis application with a low level of multi-reflection. Finally, the UVP system is demonstrated to measure the velocity vector of bubbly flow in the narrow flow channel on the rod bundle, andthe velocity vector of the bubble and liquid obtained simultaneously.

Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review

The development of modern power systems is directly related to changes in the traditional principles of management, planning, and monitoring of electrical modes. The mass introduction of renewable energy sources and control devices based on power electronics components contributes to changing the nature of the flow of transient and quasi-established electrical modes. In this area, the problem arises of conducting a more accurate and rapid assessment of the parameters of the electrical regime using synchronized vector measurement devices. The paper presents an extensive meta-analysis of the modern applications of phasor measurement units (PMUs) for monitoring, emergency management and protection of power systems. As a result, promising research directions, the advantages and disadvantages of the existing approaches to emergency management, condition assessment, and relay protection based on PMUs are identified.

Heuristic method of adaptive filtering for noisy GARCH processes

In this paper, the filtering problems of the discrete systems with noisy GARCH(1,1) processes are discussed by the heuristic method. Based on the intuition that the density function of the noisy GARCH(1,1) process can be well approximated by its driving noise density function with time-varying variance, three different approximating methods are proposed, respectively. The first is to approximate the density function of the system measurement noise by its driving noise density function with time-varying variance, then calculate the iterative state vector and measurement vector. The second is only to use its driving noise density function to approximate the density function of the state noisy GARCH(1,1) process, and then calculate the iterative state and measurement vector. The third is to use its driving noise density functions with time-varying variances to directly approximate the density functions of the noisy GARCH(1,1) processes for the state and measurement vectors. The numerical simulation results demonstrate the effectiveness of our algorithms in the noisy GARCH(1,1) environment.

Response spatiotemporal correlation and transient temperature field direct reconstruction for heat conduction system

In this paper, according to a spatiotemporal correlation characteristic of the temperature response of a transient heat conduction system, a response spatiotemporal correlation (RSTC) model of the temperatures between measurement space and estimation space of the system is proposed. Furthermore, a RSTC based direct reconstruction (RSTC-DR) method is developed for a transient temperature field reconstruction problem of a heat conduction system with unknown heat sources. In the offline stage, firstly, mapping eigenvectors of a heat conduction system are determined by the transfer equations without specific heat source information. Then, according to the correlation degree between the mapping eigenvectors, a finite number of representative spatial points (RPs) are extracted. Meanwhile, a fuzzy membership relationship between the mapping characteristics of the RPs and that of the temperature field is determined. Namely, effective mapping characteristic coverage of the RPs to the whole temperature field is realized. Moreover, a RSTC model between the measurement spatial points (MPs) and the RPs is obtained by the step response model of the temperature field. In the online stage, based on the RSTC model, the temperature responses of the RPs are estimated by employing the time series vector of the temperature measurements of the MPs. Next, according to the fuzzy membership relationship, the temperature field can be reconstructed in real time by synthesizing the temperature estimations of the RPs. The RSTC-DR method is employed to achieve the non-destructive reconstruction of the transient temperature field of a two-dimensional heat conduction system. The temperature field is reconstructed by the numerical experiment to verify the effectiveness of the proposed method and to discuss its adaptability. Furthermore, the practical effectiveness of the method is demonstrated by applying the existing experimental data.

Performance bounds of the intensity-based estimators for noisy phase retrieval

The aim of noisy phase retrieval is to estimate a signal x0∈Cd from m noisy intensity measurements bj=|〈aj,x0〉|2+ηj,j=1,…,m, where aj∈Cd are known measurement vectors and η=(η1,…,ηm)⊤∈Rm is a noise vector. A commonly used estimator for x0 is to minimize the intensity-based loss function, i.e., xˆ:=argminx∈Cd∑j=1m(|〈aj,x〉|2−bj)2. Although many algorithms for solving the intensity-based estimator have been developed, there are very few results about its estimation performance. In this paper, we focus on the performance of the intensity-based estimator and prove that the error bound satisfies minθ∈R⁡‖xˆ−eiθx0‖2≲min⁡{‖η‖2m1/4,‖η‖2‖x0‖2⋅m} under the assumption of m≳d and aj∈Cd,j=1,…,m, being complex Gaussian random vectors. We also show that the error bound is rate optimal when m≳dlog⁡m. In the case where x0 is an s-sparse signal, we present a similar result under the assumption of m≳slog⁡(ed/s). To the best of our knowledge, our results provide the first theoretical guarantees for both the intensity-based estimator and its sparse version.