Arrival Of Signals Research Articles

Weighing the spacetime along the line of sight using times of arrival of electromagnetic signals

We present a new method of measuring the mass density along the line of sight, based on precise measurements of the variations of the times of arrival (TOA's) of electromagnetic signals propagating between two distant regions of spacetime. The TOA variations are measured between a number of slightly displaced pairs of points from the two regions. These variations are due to the nonrelativistic geometric effects (Roemer delays and finite distance effects) as well as the gravitational effects in the light propagation (gravitational ray bending and Shapiro delays). We show that from a sufficiently broad sample of TOA measurements we can determine two scalars quantifying the impact of the spacetime curvature on the light propagation, directly related to the first two moments of the mass density distribution along the line of sight. The values of the scalars are independent of the angular positions or the states of motion of the two clock ensembles we use for the measurement and free from any influence of masses off the line of sight. These properties can make the mass density measurements very robust. The downside of the method is the need for extremely precise signal timing.

Anomalous dispersion observed in signal arrivals at a deep-sea floor receiver.

An anomalous dispersion, e.g., when low frequencies arrive earlier whereas the high frequencies arrive later, was observed in the signal arrivals recorded by a single deep-sea bottom-mounted vector sensor. Numerical simulations and modal analyses, based on a three-layer range-independent model, are applied to interpret the anomalous dispersion. Results indicate that the arrival with anomalous dispersion corresponds to trapped modes in the low sound speed sediment and can be observed when both the source and receiver are deployed near the seafloor. Furthermore, the cutoff frequencies, dispersion characteristics, and energy distributions of trapped modes are also performed in this paper.

Sound source localization based on improved adaptive beamforming

In adaptive systems, the least mean square error (LMS) adaptive algorithm is widely used. The variable step size LMS algorithm (SVSLMS) based on the Sigmoid function has greatly improved the convergence speed, and the normalized LMS algorithm is stable Sexual performance has certain advantages. Therefore, improvement is made on this basis, the SVSLMS algorithm is normalized, and the iteration speed is increased by changing the iteration factor, and the stability of the algorithm is also improved. This paper uses MATLAB simulation tools to verify the superiority of the new algorithm. Finally, the improved algorithm is applied to beamforming to estimate the azimuth of arrival of the simulated sound source signal at the partial discharge point of the submarine power cable.

Analysis of the directional pattern of a non-equidistant antenna array with a random arrangement of radiating elements in three-dimensional space

A three-dimensional antenna array with a spherical surface was investigated. The channels were randomly placed at array points with a vertical and horizontal step equal to 0.45 wavelengths. For uniform filling of the sphere, the minimum allowable distance between the channels was introduced, depending on the number of channels and the sphere size. The maximum side lobe level depends on the number of channels. To calculate the path difference in a given direction, the coordinate system was rotated so that one of the coordinate axes coincided with the selected direction. To find the radiation pattern, the scalar product of the amplitude-phase distribution vector corresponding to the signal emission direction was calculated by the complex-conjugate vector of the amplitude-phase distribution corresponding to all possible directions of signal arrival from an external source.

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Functional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

Simulation of operation of dual-frequency antenna array of GLONASS consumer navigation equipment under influence of simulated interference

The article deals with the methods of increasing the noise immunity of the navigation equipment of the consumer under the influence of deliberate imitation interference (spoofing). The main differences between imitation interference and powerful noiselike interference are highlighted. The initial data for computer modeling of various signal-interference conditions are formed, with makes it possible to conduct a comparative analysis of various methods of interference suppression. The created model allows us to take into account the real coordinates of navigation satellites relative to the selected observation point, the geometry of the antenna array of the navigation receiver and its main functional characteristics. The possibility of using antenna arrays with a controlled diagram-forming scheme for focusing the radiation pattern in the directional of arrival of satellite navigation signals and creating a «zero» in the direction of arrival of interference is investigate. The dependence of the potential level of interference suppression on the elevation of interference arrival is analyzed. A comparison of two optimization methods used as an interference reduction algorithm is presented.

Sound Localization for Ad-Hoc Microphone Arrays

Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

Indian classical musical instrument classification using Timbral features

SummaryMusical instrument classification becomes effective when the music signal arrives with profound characteristics. This urged the researchers to develop an automatic system of recognizing the music signals and classify the instruments interplayed through the music. Thus, this paper proposes a model for the Indian music classification system using the optimization‐based stacked autoencoder. The significance of this research is based on the proposed Cuckoo‐dragonfly optimization (CuDro)‐based stacked autoencoder, where the proposed CuDro optimization trains the stacked autoencoder for acquiring accurate classification results. The proposed CuDro technique is the combination of the standard Cuckoo search (CS) and the Dragonfly algorithm (DA) that renders optimal weights for training the stacked autoencoder (SAE). Moreover, the musical instrument classification using the proposed CuDro‐based stack autoencoder is based on the compact features, such as Timbral features and proposed FrMkMFCC features, which further add value to this research. The Timbral features like Spectral flux, spectral kurtosis (SK), Spectral skewness, Spectral pitch similarity, Roughness, In harmonicity are added in the research for efficient musical instrument classification. The proposed FrMkMFCC feature is the integration of the Fractional Fourier transforms and Multi kernel method, and Mel Frequency Cepstral Coefficient (MFCC) features. The analysis using the developed classification methodology confirms that the proposed method acquired the maximum accuracy of 96.16%, the sensitivity of 86.86%, and specificity of 92.85%, respectively.

A MSWF root‐MUSIC based on Pseudo‐noise resampling technique

This paper uses the shift-invariance property of uniform linear array in root-MUSIC estimator for obtaining signal and noise subspaces by applying multistage Wiener filter (MSWF) procedure. Also, the MSWF root-MUSIC based on the pseudo-noise resampling process for estimating the direction of arrival (DOA) of signals is proposed. By this process, a root estimator bank and a corresponding DOA estimator bank are constructed. Then, a hypothesis test is applied to the DOA estimator bank to detect the normal DOA estimators from abnormal DOA estimators called outliers. By averaging the corresponding root estimators of normal DOA estimators, the final DOAs can be determined more accurately. When all the DOA estimators fail to pass the hypothesis test, the criterion based on the Gaussian weight average of the root estimator bank is introduced. By applying this criterion, better outlier-free performance of MSWF root-MUSIC can be obtained. Simulations show that our method can improve the DOA estimations, especially in small sample sizes and low signal-to-noise ratios.

Arcing fault diagnosis using first peak arrival of EM radiation signal

The objective of this study was to diagnose the arcing fault signals based on the first peak of arrival method using antenna to assess its use as potential arcing fault detection in power system network. Square patch antenna and circle patch antenna were employed for detection on artificial arcing in real environment. First peak of arcing signal arrival was measured through an analysis over a range of time and amplitude signals detected. For accurate results, Discrete Wavelet Transform (DWT) denoising technique was applied to the arcing signals detected as denoising tools. Analysis of first peak of signal arrival time and amplitude were carried out using MATLAB software to measure the changes in signals detected caused by di different placements of antenna. The results revealed that the first peak of signal arrival time, amplitude, type of antenna used and placement of the antenna around arcing source point all reflect the signals measurement.

Improving Accuracy of Source Localization Algorithms Using Kalman Filter Estimator

Passive geolocation of emitters provides benefits to military and civilian surveillance and in the field of aerospace. Passive geolocation can be done by exploiting some of the parameters of the signal transmitted by the source or emitter. In this paper the Time Difference Of Arrival (TDOA) of the signal at different receivers is used to estimate the position of the emitter. This paper aims at presenting the research for the development of an algorithm which can be used in a distributed network of acoustic sensors consisting of an emitter and five stationary receivers. By means of measurement of time difference of arrival of underwater acoustic signal the source position is estimated using three different algorithms. Taylor series, closed form and two stage weighted least square methods are used to estimate the stationary source location by TDOAmeasurements of received signal and compared on the basis of Cramer Rao Lower Bound (CRLB). Comparison of performance between these methods are done and analysed.Use of a kalman filter is proposed to improve the accuracy of all the three methods and the results are compared and analysed.

Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility

In this paper, we propose an RFID-based location-positioning platform for managing cart operations at vast and fast-moving distribution facilities. Our work was motivated by a real-world problem in a large airmail center. Our system requires that each cart is affixed with an active RFID tag, and RFID readers are installed at multiple locations of the facility. The locations of the tagged carts are determined by the estimated angles of arrival communication signals between the readers and the tags and the signal strengths, and the information about their positions are stored in the platform continuously. This platform enables the users to locate tagged objects in real time, thereby saving staff members a significant amount of time from searching for the right carts in the highly dynamic environment. Consequently, the mail facility can benefit from the increased efficiency. Furthermore, the system is also able to reduce the chance of having a delay in delivering mail bags to unit load device (ULD) area and misplacements of mail bags to ULDs. We further analyze the the huge amount of historical data collected from the RFID infrastructure of the airmail center for the cart movements within the facility and observed previously unrecognized operational issues. We also discuss some of the challenges and problems faced in this project. We believe that this application can bring huge benefits to businesses and organizations along supply chains by effectively anticipating the increasing demand for logistics services in the age of electronic retailing.

Observation of the long-period monotonic seismic waves of the November 11, 2018, Mayotte event by Iranian broadband seismic stations

On November 11, 2018, an event generating long-lasting, monotonic long-period surface waves was observed by seismographs around the world. This event occurred at around 09:28 UTC east of the Mayotte Island, in the Indian Ocean off the coast of East Africa. This event is unusual due to the absence of body waves in the seismograms and no feeling of earth shaking by people locally. The purpose of this study is to investigate this unusual event using the waveforms recorded by 26 stations of the Iranian National Broadband Seismic Network. The stations are located at epicentral distances ranging from 4542 to 5772 km north-northeast of the event’s epicenter. The arrival of monochromatic long-period signals is visible around 10 UTC in the recordings of all the stations and the signals lasted for more than 30 min. Frequency analysis of the seismograms shows a clear peak at 0.064 Hz (15.6 s/cycle). The maximum amplitude of the transverse components is less than a half of the radial components. This is in agreement with the theoretical radiation pattern of Rayleigh and Love waves at a frequency of 0.06 Hz for a vertical compensated linear vector dipole source mechanism. The average apparent phase velocities were calculated as 3.31 and 2.97 km/s, in the transverse and radial directions, corresponding, respectively, to Love and Rayleigh waves in the frequency range of 0.05–0.07 Hz. A surface wave magnitude of Ms 5.07 ± 0.22 was estimated. Just before the monochromatic signal arrives, there is some dispersion in the surface waves. This observation may suggest a regular earthquake of Ms 4.3 ± 0.11 that triggered the November 11, 2018, event. The difference between the arrival times of the recorded surface waves of the two events is estimated to be less than 31 s, and most likely of ~ 7 s only.

Dual-band (28/38 GHz) MIMO Antenna System for 5G Mobile Communications with Efficient DoA Estimation Algorithm in Noisy Channels

In this paper, a dual-band (28/38 GHz) linear antenna arrays of four and eight elements are proposed to work as a MIMO arrays for the 5G communication systems. Each element in the array is a dual-band Yagi-Uda antenna designed to operate at 28 and 38 GHz. The eight-elements array size has a total dimension of 79.4 mm x 9.65 mm excluding the feeding microstrip line. The maximum gain of the array is about 18 dB. The peaks of correlation at matched angles (PCMA) technique is applied to determine the direction of arrival for multiple incoming signals. The effects of phase noise and additive Gaussian noise on the error in the DoA estimation are studied showing good accuracy of the PCMA algorithm. Numerical and experimental investigations are achieved to assess the performance of both the single-element antenna and the eight-element MIMO linear antenna array. It is shown that the simulation results agree with the experimental measurements and both show good performance of the single antenna as well as the MIMO linear array system. The envelope correlation coefficient (ECC) and the diversity gain (DG) are calculated and the results show that the proposed MIMO antenna system is suitable for the forthcoming 5G mobile communications. The radiation patterns for single antenna and four-element array are measured and compared to the electromagnetic simulation results showing good agreement.

Array Manifold Measurement and Verification for UCA Calibration in Multistatic PCL System based on FM Broadcasting

This study describes a method of measurement and verification of array manifold of uniform circular array antenna applicable to multistatic Passive Coherent Location(PCL) system using FM broadcasting. In an environment of outdoor test where FM broadcast signals are scattered, array manifold measurement methods using network analyzer and multi-channel digital receiver are introduced. Also, the descriptions and solutions for the test limits of each measurement method and the considerations affecting the measurement accuracy are presented. In addition, to verify the validity of the measured array manifold, the gain and phase difference were compared with the array manifold data obtained by EM simulation, and the effectiveness and accuracy of the measured array manifolds were compared and analyzed by estimating the direction of arrival of the FM broadcast signal received from the multistatic PCL system. Keywords: Multistatic PCL, Uniform Circular Array, Array Manifold, DOA Estimation Keywords: 멀티스태틱 PCL, 원형 배열 ì•ˆí Œë‚˜, ì–´ë ˆì´ 매니폴드, 도래각 ì¶”ì •

Sequential sparse Bayesian learning for beamforming

A sequential Bayesian method for beamforming is presented for the estimation of the directions of arrivals (DOAs) of source signals which are varying over time. The sparse Bayesian learning (SBL) uses prior information of unknown source amplitudes as a multi-variate Gaussian with zero-mean and time-varying variance parameters. For sequential processing, we utilize the unknown variance as statistical information across time and propose a sequential SBL-based method to improve time-varying DOA estimation performance. The suggested method has two steps. A prediction step calculates the prior distribution of the current variance parameter from the variance parameter estimated at the previous time step, and an update step incorporates the current measurements. This SBL approach with sequential processing provides high-resolution for time-varying DOA tracking. We evaluate the proposed method using simulated data and real data from the SWellEx-96 experiment.

A Physics-Based Signal Processing Approach for Noninvasive Ultrasonic Characterization of Multiphase Oil-Water-Gas Flows in a Pipe.

A signal processing technique is presented for determining the composition of multiphase oil-water-gas flow in a pipe using noninvasive ultrasonic speed of sound measurements from a transmitter-receiver pair bonded to diametrically opposite sides of a pipe. A linear chirp excitation is used to send broadband ultrasonic energy that propagates in two paths from transmitter to receiver such as: 1) a wave through the pipe wall and then the multiphase mixture and 2) ultrasonic guided waves along the pipe wall in the circumferential direction. As the ultrasonic attenuation of the multiphase mixture increases, the amplitude of the signal through the fluid mixture decreases relative to that of circumferential guided waves, making it difficult to determine the time of arrival of the fluid-path signal and, hence, the speed of sound in the mixture. The proposed signal processing technique overcomes this challenge by using: 1) a guided wave subtraction approach to suppress the strength of guided wave signals relative to the fluid-path signal and 2) a Gaussian reconstruction approach for synthetic enhancement of the fluid-path signal by output signal reconstruction at frequencies corresponding to peak transmission of ultrasonic energy. The efficacy of the technique is demonstrated using experiments carried out in a field-scale flow loop with varying compositions of oil-water-gas mixtures. It is shown that the proposed approach can enhance the signal detectability by approximately 20 dB in comparison with the traditional approach that does not utilize guided wave subtraction and also improves the gas tolerance of composition measurements up to 20% in gas volume fraction.

Detection of gravitational waves using Bayesian neural networks

We propose a new model of Bayesian Neural Networks to not only detect the events of compact binary coalescence in the observational data of gravitational waves (GW) but also identify the full length of the event duration including the inspiral stage. This is achieved by incorporating the Bayesian approach into the CLDNN classifier, which integrates together the Convolutional Neural Network (CNN) and the Long Short-Term Memory Recurrent Neural Network (LSTM). Our model successfully detect all seven BBH events in the LIGO Livingston O2 data, with the periods of their GW waveforms correctly labeled. The ability of a Bayesian approach for uncertainty estimation enables a newly defined `awareness' state for recognizing the possible presence of signals of unknown types, which is otherwise rejected in a non-Bayesian model. Such data chunks labeled with the awareness state can then be further investigated rather than overlooked. Performance tests with 40,960 training samples against 512 chunks of 8-second real noise mixed with mock signals of various optimal signal-to-noise ratio $0 \leq \rho_\text{opt} \leq 18$ show that our model recognizes 90% of the events when $\rho_\text{opt} >7$ (100% when $\rho_\text{opt} >8.5$) and successfully labels more than 95% of the waveform periods when $\rho_\text{opt} >8$. The latency between the arrival of peak signal and generating an alert with the associated waveform period labeled is only about 20 seconds for an unoptimized code on a moderate GPU-equipped personal computer. This makes our model possible for nearly real-time detection and for forecasting the coalescence events when assisted with deeper training on a larger dataset using the state-of-art HPCs.

A Study of Delay Estimation Methods at Signalized Intersections for Mixed Traffic Condition

An effective traffic control measure is the one that can significantly reduce delays incurred due to rising traffic congestion and improves travel time reliability. To achieve this, an accurate estimation of delay is very critical. The method of delay estimation varies with the type of data source available, and the type of data collected. This paper aims at studying various methods of delay estimation at an intersection for data types from three data sources—location-based data (videography), Wi-Fi sensor data, and GPS based probe data. The challenges associated with executing each of these methods are also discussed. Besides, a scalable and reliable data source among the three was chosen to calibrate and validate the delay equation suggested by Highway Capacity Manual 2000 (HCM 2000) to suit Indian traffic conditions. A linear regression model was fitted for the progression factor (PF) of the HCM delay equation with an R squared value of 0.85. Validation of the calibrated model yielded an average Mean Absolute Percentage Error (MAPE) of 12.59%. The calibrated model can be used for the estimation of delay based on historic traffic arrival patterns and signal timings.

Locating Thunder Source Using a Large-Aperture Micro-Barometer Array

Lightning generates sound waves across a wide range of frequencies, including infrasonic waves below 20 Hz. Source mechanism for these low frequency pulses is still area for debate. Infrasound pulses detected after rapid changes of electrostatic field during the thunderstorm activity were analyzed. The measurements were done by large aperture array of absolute microbarometers located in the Western part of the Czech Republic. Distances between four measuring sites are in the range of 4–10 km. The infrasound source position was calculated from time delays between the rapid change of electrostatic field and infrasound signal arrival to the individual microbarometers assuming propagation of spherical waves from the source. Only cases with a sufficient signal-to-noise ratio on all four microbarometers were analyzed. The variation of sound speed with height due to temperature height profile was taken into account. For most of the analyzed cases, the calculated infrasound source position corresponds to the lightning location determined by European lightning detection network (EUCLID). The calculated height of infrasound source is most often 3–5 km.