Delay Estimation Research Articles

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Acoustic signal detection technology has significant advantages in detecting the leakage and bursting of heat exchange pipes in boilers. To address the current lack of sound signal data for steam medium leakages and the problem of large errors in the complex sound field environment of power plants, we performed an innovative experimental comparative analysis of leakage acoustic signals under a dual medium of compressed air and steam to provide a reference for detecting leakage acoustic signals. During the experiment, the time and frequency domains were analyzed by changing the pressure of the leakage pipeline and aperture of the leakage hole, and the spectrogram and power spectrum of the leakage acoustic signal were obtained using fast Fourier transform and autocorrelation analysis. The results showed that the signal value of the leakage medium increased with increasing pipeline pressure and leakage aperture and that the energy of the steam leakage acoustic signal was greater than that of compressed air under the same pressure and aperture because of its larger specific heat capacity. In addition, the complete empirical mode decomposition of adaptive noise (CEEMDAN) algorithm was introduced into the denoising decomposition of the leakage sound signal in the furnace, and the average error of the time delay value of the leakage acoustic signal calculated using the CEEMDAN algorithm was observed to be within 5%.

Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models

Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times.

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

AbstractAccurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

Multiband Delay Estimation for Localization Using a Two-Stage Global Estimation Scheme

The time of arrival (TOA)-based localization techniques, which need to estimate the delay of the line-of-sight (LoS) path, have been widely employed in location-aware networks. To achieve a high-accuracy delay estimation, a number of multiband-based algorithms have been proposed recently, which exploit the channel state information (CSI) measurements over multiple non-contiguous frequency bands. However, to the best of our knowledge, there still lacks an efficient scheme that fully exploits the multiband gains when the phase distortion factors caused by hardware imperfections are considered, due to that the associated multi-parameter estimation problem contains many local optimums and the existing algorithms can easily get stuck in a “bad” local optimum. To address these issues, we propose a novel two-stage global estimation (TSGE) scheme for multiband delay estimation. In the coarse stage, we exploit the group sparsity structure of the multiband channel and propose a Turbo Bayesian inference (Turbo-BI) algorithm to achieve a good initial delay estimation based on a coarse signal model, which is transformed from the original multiband signal model by absorbing the carrier frequency terms. The estimation problem derived from the coarse signal model contains fewer local optimums and thus a more stable estimation can be achieved than directly using the original signal model. Then in the refined stage, with the help of coarse estimation results to narrow down the search range, we perform a global delay estimation using a particle swarm optimization-least square (PSO-LS) algorithm based on a refined multiband signal model to exploit the multiband gains to further improve the estimation accuracy. Simulation results show that the proposed TSGE significantly outperforms the benchmarks with comparative computational complexity.

Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks: A preliminary investigation

The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System (GNSS). Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services. The objective of this study is to propose a neural network, trained with radio occultation data from the COSMIC-1 mission, that generates average ionospheric electron density profiles during dusk, focusing on the pre-reversal enhancement of the zonal electric field. Results show that the estimated profiles exhibit a clear seasonal pattern, and reproduce adequately the climatological behavior of the ionosphere, thus presenting strong appeal on ionospheric error attenuation.

RouteNet-Fermi: Network Modeling With Graph Neural Networks

Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles — e.g., with complex non-Markovian models — and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network.

Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM)

Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM)

Intermittency of density fluctuations and zonal-flow generation in MAST edge plasmas

The properties of the edge ion-scale turbulence are studied using the beam emission spectroscopy (BES) diagnostic on MAST. Evidence of the formation of large-scale high-amplitude coherent structures, filamentary density blobs and holes, 2–4 cm inside the plasma separatrix is presented. Measurements of radial velocity and skewness of the density fluctuations indicate that density holes propagate radially inwards, with the skewness profile peaking at 7–10 cm inside the separatrix. Poloidal velocities of the density fluctuations measured using cross-correlation time delay estimation (CCTDE) are found to exhibit an intermittent behaviour. Zonal-flow analysis reveals the presence of poloidally symmetric coherent oscillations – low-frequency (LF) zonal flows and geodesic acoustic modes (GAM). Shearing rates of the observed zonal flows are found to be comparable to the turbulence decorrelation rate. The observed bursts in density-fluctuation power are followed by quiescent periods with a transient increase in the power of sheared flows. Three-wave interactions between broadband turbulence and a GAM are illustrated using the autobispectral technique. It is shown that the zonal flows and the density-fluctuation field are nonlinearly coupled and LF zonal flows mediate the energy transfer from high- to low-frequency density fluctuations.

Fault localization on power cables using time delay estimation of partial discharge signals

Precise localization of partial discharge (PD) sources on power cables is vital to prevent power line failures that can lead to significant economic losses for electrical suppliers. This study proposes four methods to estimate the time delay of PD signals under electromagnetic interference, including white Gaussian noise (WGN) and discrete sinusoidal interference (DSI), using denoised PD signals with signal-to-noise ratios ranging from 10.6 to -7.02 dB. The maximum peak detection (MPD) and cross-correlation (CC) approaches, as well as two new techniques, interpolation cross-correlation (ICC) and envelope cross-correlation (ECC), are evaluated for their effectiveness in PD source localization. The researchers employ the time difference of arrival (TDoA) algorithm to compute PD location using the double-end PD location algorithm, where the PD location precision serves as an indicator of the accuracy of the time delay estimation methods. The study concludes that CC and ICC are the most suitable methods for estimating the time delay of PD signals in the PD location algorithm, as they exhibit the lowest error rates. These results suggest that CC and ICC can be used effectively for precise PD source localization under electromagnetic interference on power cables.

Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization

In fisheries science research and farmed fish monitoring, acquiring individual fish echoes is the basis for the growth assessment, tracking, and target strength estimation of farmed fish. However, individual fish echo detection methods based on narrowband signal features cannot be applied well to high-density aquaculture scenarios. The broadband signaling system can improve the distance resolution of the detected target and can help to improve the performance of individual fish echo detection. In this study, for the broadband signal system and the characteristics of the underwater fish acoustic echoes, an individual fish echo detection method is proposed using the matched filter output envelope peak interval and instantaneous frequency characteristics of the echo as evaluation indices, and the simulation and experiments of the method are carried out in an anechoic water tank. The results show that the broadband signal system and the corresponding detection method perform better in detecting single target echoes than the narrowband signal system. Compared with the broadband single echo detection method that only relies on the peak interval of the matched filter envelope, the joint detection method that incorporates the instantaneous frequency characteristics of the echo signal has a better rejection capability for overlapping echoes, which can reduce the probability of misjudging the overlapping echoes. The combined detection methods may provide a better detection performance for individual fish echoes.

Distributed extended state observer‐based fault‐tolerant control for nonlinear multi‐agent systems with switching topologies

AbstractIn this paper, a novel fault‐tolerant control scheme is designed and analyzed for multi‐agent systems (MASs) with incipient faults, disturbances, parameter uncertainties, time delay, and nonlinear terms, and the proposed MAS with switching communication topologies. Firstly, the proposed distributed extended state observer estimates the incipient fault and system states in follower agents, which extends the incipient fault vector to a new system state. Meanwhile, the designed observer considers the output errors of itself and other agents. Further, a state estimation feedback‐based fault‐tolerant controller is constructed, composed of a delay estimator and a non‐delay estimator to guarantee the proposed MASs have good operation performance. Then, some stability analysis conclusions of the MAS with incipient faults, time delay, disturbances, parameter uncertainties, and nonlinear terms are obtained. The proposed results can also be applied to practical systems like the wireless power transfer system. Finally, two simulation results are provided to demonstrate the effectiveness of the proposed fault‐tolerant control technique.

Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations

Multi-Global Navigation Satellite Systems (multi-GNSS) (including GPS, BDS, Galileo, and GLONASS) provide a significant opportunity for high-quality zenith tropospheric delay estimation and its applications in meteorology. However, the performance of zenith total delay (ZTD) retrieval from single- or multi-GNSS observations is not clear, particularly from the new, fully operating BDS-3. In this paper, zenith tropospheric delay is estimated using the single-, dual-, triple-, or four-GNSS Precise Point Positioning (PPP) technique from 55 Multi-GNSS Experiment (MGEX) stations over one year. The performance of GNSS ZTD estimation is evaluated using the International GNSS Service (IGS) standard tropospheric products, radiosonde, and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). The results show that the GPS-derived ZTD time series is more consistent and reliable than those derived from BDS-only, Galileo-only, and GLONASS-only solutions. The performance of the single-GNSS ZTD solution can be enhanced with better accuracy and stability by combining multi-GNSS observations. The accuracy of the ZTD from multi-GNSS observations is improved by 13.8%, 43.8%, 27.6%, and 22.9% with respect to IGS products for the single-system solution (GPS, BDS, Galileo, and GLONASS), respectively. The ZTD from multi-GNSS observations presents higher accuracy and a significant improvement with respect to radiosonde and ERA5 data when compared to the single-system solution.

Intelligent joint multi-parameter optical performance monitoring scheme based on HT images and MT-ResNet for elastic optical network

In elastic optical network (EON), joint multi-parameter optical performance monitoring (OPM) can effectively manage, diagnose, and reduce operational costs for transmission optical links. In this paper, we propose a novel joint monitoring scheme utilizing Hough transform (HT) images combined with multi-task residual neural network (MT-ResNet) for EON. The scheme can realize baud rate identification (BRI), modulation format identification (MFI), residual chromatic dispersion identification (CDI), optical signal-to-noise ratio (OSNR) and residual differential group delay (DGD) estimation at the same time. The HT image is obtained by preprocessing original constellation diagram, which is a key feature of parameter monitoring for EON signals and presents obvious differentiation in parameter space. By optimizing the skip connections in MT-ResNet, we effectively resolve the issue of details information loss or incompleteness caused by the transmission of impaired optical signals in the neural network. The simulation results demonstrate that the identification success rate can reach 100 % for two common BRs, five mainstream MFs, and seven residual CD values with different impairment degrees. The mean absolute errors (MAEs) of OSNR and residual DGD estimates are 0.42 dB and 0.014 times symbol period respectively. The scheme has excellent tolerance for fiber nonlinear effects. In experimental verification, the accuracies of BRI and MFI are 100 %, and the MAEs of corresponding OSNR estimation for PDM-QPSK/16QAM/32QAM are 0.25 dB, 0.36 dB, and 0.40 dB, respectively. Compared with the existing typical schemes, our scheme significantly improves performance and reduces complexity while simultaneously monitoring a large number of parameters.

Modeling and Forecasting Freight Delivery for Blocked and Unblocked Streets through Machine Learning Approaches and Analysed by Microsimulation Model

Deliveries of freight on signalized city roads often lead to lane obstructions, contributing to urban traffic congestion. This issue has garnered increasing attention as traffic engineers and city planners seek sustainable solutions to meet growing demand while working within the constraints of limited road capacity. The primary objective of this study is to assess a model designed to quantify the impact of freight deliveries on both the capacity and delay experienced on signalized city roads in Ahmedabad. The model is structured similarly to the approach outlined in the Highway Capacity Manual (HCM2010). The aim of this research is to provide insights into the utilization of these analytical tools in the context of urban freight delivery policies. This research explores the estimation of delay and vehicle capacity, accounting for various factors such as delivery locations, durations, and their distinct effects on different lanes. To predict vehicle capacity and estimate delays, this study employed machine learning techniques, specifically Support Vector Machines and Artificial Neural Networks. The results demonstrate a high level of agreement between our predictions and actual observations.

Precision estimation of time delay based on weak measurement with real weak value

Precision estimation of time delay based on weak measurement with real weak value

Uncalibrated phase delay estimation and ambiguity resolution for BDS-3 six-frequency uncombined PPP with legacy B1I/B3I and new B1C/B2a/B2b/B2a + b signals

Multi-frequency integration can accelerate the ambiguity resolution (AR) and shorten the convergence time of precise point positioning (PPP). BDS-3 satellites can broadcast legacy B1I/B3I and new B1C/B2a/B2b/B2a + b signals, which facilitates the multi-frequency PPP. An uncalibrated phase delay (UPD) estimation method and a fast AR method are extended for BDS-3 six-frequency uncombined PPP. The characteristics of UPD estimates are analyzed in terms of stability, ambiguity residuals, and usage rate. The data sets on seven consecutive days from 224 globally distributed stations are selected for UPD estimation, and those from 21 stations are employed for PPP AR. The results indicate that the static convergence time of BDS-3 six-frequency PPP ambiguity-fixed solutions (with a threshold of 10 cm) can reach 8.2, 4.0, and 13.4 min in the east, north, and up directions, respectively, and the three-dimensional (3D) convergence time is shortened by 21.1 % and 26.5 % compared with the six-frequency ambiguity-float solutions and the dual-frequency ambiguity-fixed solutions, respectively. The corresponding kinematic convergence time can reach 13.0, 7.0, and 33.2 min in three directions, respectively, and the corresponding improvement rates are 21.0 % and 33.7 %, respectively. In addition, the convergence time of BDS-3 six-frequency PPP ambiguity-fixed solutions can be slightly shortened even compared with the five-frequency case. The static and kinematic positioning accuracy of BDS-3 six-frequency PPP ambiguity-fixed solutions can reach 4, 3, and 9 mm, and 18, 14, and 38 mm in three directions, respectively, which is slightly better than other cases.

Enhanced Needle Visualization with Reflection Tuned Apodization based on the Radon Transform for Ultrasound Imaging.

In ultrasound (US) guided interventions, accurately tracking and visualizing needles during in-plane insertions is a significant challenge due to strong directional specular reflections. These reflections violate the geometrical delay and apodization estimations in the conventional delay and sum beamforming (DASB) degrading the visualization of needles. This study proposes a novel reflection tuned apodization to address this issue and facilitate needle enhancement through DASB. The method leverages both temporal and angular information derived from the Radon transforms of the RF data from plane-wave imaging to filter the specular reflections from the needle and their directivity. The directivity information is translated into apodization center maps through time-to-space mapping in the Radon domain which is subsequently integrated into DASB. We assess the influence of needle angulations, projection angles in the Radon transform, needle gauge sizes, and the presence of multiple specular interfaces on the approach. The analysis shows that the method surpasses conventional DASB in enhancing the image quality of needle interfaces while preserving the diffuse scattering from the surrounding tissues without significant computational overhead. The work offers promising prospects for improved outcomes in US guided interventions and better insights into characterizing US reflections with Radon transforms.

Super-Resolution Time Delay Estimation of OFDM Passive Radar Based on Fully Convolutional Networks

Super-Resolution Time Delay Estimation of OFDM Passive Radar Based on Fully Convolutional Networks

Low complexity joint angle and delay estimation for underwater multipath acoustic channel based on sparse Bayesian learning

In the field of underwater acoustic communication, multipath signals arrive at the receiving end with different angles and path delays, which can cause degradation in communication quality. When performing angle-delay joint estimation, sparse Bayesian learning (SBL) has many advantages over traditional methods, such as high accuracy, resolution and global minimum results, but at the cost of high computational complexity. Regarding this, in this study, we propose a low-complexity SBL based framework to jointly estimate the angle and delay of the received signal. We first propose a low-complexity approximation SBL algorithm applied to joint estimation, which can keep the super-resolution and estimation accuracy but significantly reduce the computation time. To further tackle the problem of performance degradation of the approximate algorithm in underwater low signal-to-noise ratio environments, we utilize above result as a starting point for the space-alternating generalized Expectation Maximization (SAGE) algorithm to achieve a more precise estimation. Simulation and experimental results demonstrate that the proposed algorithm has an excellent performance in various underwater multipath scenarios.

Fiber Optic Sensor Based on Linear Sagnac Interferometer for Branch Localization

One of the challenges of distributed fiber optic sensors is to monitor objects that have multiple branches, such as pipelines or power grids. Existing sensors require multiple sensing systems to cover each branch, which increases the cost and complexity of the monitoring. We propose a fiber optic sensor based on linear Sagnac interferometer for branch localization for practical environments where branch paths exist. The system is based on a dual-wavelength single-core Sagnac interferometer, which can extend multiple branches in the sensing cable part. The phase difference signals caused by external disturbances are demodulated and go through different signal processing. By using time delay estimation and null frequency method, we can determine the exact location of the disturbance on the main or branch path. Moreover, we propose a new method to reduce interference with null frequencies of the spectrum by subtracting the phase difference signal at one wavelength from the other. We demonstrate the feasibility and performance of our proposed system through theoretical analysis and experimental results. Our proposed system offers a simple and effective solution for branch localization in distributed fiber optic sensing applications.