Delay Estimation Research Articles

An experimental and kinetic modeling study of the auto-ignition delay times of trimethyl phosphate-in-air mixtures

Organophosphorus compounds (OPCs) are known to be combustion inhibitors (CI), fire suppressants, or flame retardant molecules (FRMs) for polymers and as surrogates (simulants) for the disposal or thermal degradation of chemical war agents (CWAs). Despite a significant number of studies on the mechanism of their action, OPCs’ combustion chemistry is still insufficiently understood. There is a need for further understanding of their auto-ignition and oxidation characteristics at relevant conditions (high pressures and low temperatures). This study reports on new data on the autoignition delays of Trimethyl Phosphate (TMP)-in-air mixtures obtained from experiments performed on a high-pressure shock tube (HPST) at pressures of 5 and 10 bar in the initial temperature range from 1200 to 2200 K. An updated TMP kinetic model deduced from the Glaude et al. model for the thermal degradation of OPCs is also proposed for the estimation of the autoignition delays of the studied mixtures by incorporating new reaction pathways and corresponding rate constants estimation of some reactions involving TMP and some intermediate products of its degradation. The results indicate that the proposed model is in satisfactory agreement with all the investigated mixtures.

Effects of time delay on the collective behavior of globally coupled harmonic oscillators with fluctuating frequency

In the viscoelastic environment, coupling forces between particles often play an essential role. Due to the time delay in the transmission of coupling forces, this paper focuses on investigating the effects of time delay on the collective behaviors of coupled harmonic oscillators, including synchronization, stability, and stochastic resonance. Firstly, we establish a time delay decoupling formula that can complete the accurate solution of the model under a large delay, thus breaking the limitation of previous small delay approximation methods. Secondly, the statistical synchronization between particles is derived, which means the mean-field behavior of the system can be studied through single-particle behavior. Thirdly, the system’s stability condition is given, demonstrating that the stability region expands with increasing time delay. Finally, the output response amplitude gain of the system is obtained, and then the stochastic resonance behavior of the system is studied. In the small delay region, noise competes with the ordered driving force, resulting in the classical stochastic resonance (CSR) behavior consistent with previous research findings. In the large delay region, periodic matching between the time delay and driving frequency gives rise to parameter-induced stochastic resonance (PSR) behavior, which has not been observed in previous studies.

Novel reaching law based predictive sliding mode control for lateral motion control of in‐wheel motor drive electric vehicle with delay estimation

AbstractThe lateral motion control of an in‐wheel motor drive electric vehicle (IWMD‐EV) necessitates an accurate measurement of the vehicle states. However, these measured states are always affected by delays due to sensor measurements, communication latencies, and computation time, which results in the degradation of the controller performance. Motivated by this issue, a novel reaching law based predictive sliding mode control (NRL‐PSMC) is proposed to maintain the lateral motion control of the IWMD‐EV subjected to unknown time delay. Initially, a PSMC framework is built, in which a predictor integrating with the sliding mode control is designed to eliminate the effect of time delay and generate the virtual control signals. Further, to alleviate the chattering phenomenon, a novel‐reaching law is developed, enabling the vehicle to track the desired states effectively. Subsequently, a dynamic control allocation technique is presented to optimally allocate the virtual control input to the actual control input. The accurate estimation of the aforementioned unknown delay is realized through a delay estimator. Finally, simulation and hardware‐in‐the‐loop experiments are performed for three specific driving manoeuvres, and the results demonstrate the effectiveness of the proposed controller design.

A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes

Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5). The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the global pressure and temperature 3 (GPT3) model, exhibiting 26 % and 53 % lower bias and rms, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It is expected that the application of this new model will provide improved services for high-precision global navigation satellite system (GNSS) positioning and GNSS meteorology.

Improving the Robustness of Time Difference of Arrival Estimation Based on the Energy Center of Gravity Rearrangement.

An accurate estimation of the time difference of arrival (TDOA) is crucial in localization, communication, and navigation. However, a low signal-to-noise ratio (SNR) can decrease the reliability of the TDOA estimation result. Therefore, this study aims to improve the performance of the TDOA estimation of dual-channel sensors for single-sound sources in low-SNR environments. This study introduces the theory of time rearrangement synchrosqueezing transform (TRST) into the time difference of arrival estimation. While the background noise TF points show random time delays, the signal time-frequency (TF) points originating from uniform directions that exhibit identical lags are considered in this study. In addition, the time difference rearrangement synchrosqueezing transform (TDST) algorithm is developed to separate the signal from the background noise by exploiting its distinct time delay characteristics. The implementation process of the proposed algorithm includes four main steps. First, a rough estimation of the time delay is performed by calculating the partial derivative of the short-time cross-power spectrum. Second, a rearrangement operation is conducted to separate the TF points of the signal and noise. Third, the TF points on both sides of the time-delay energy ridge are extracted. Finally, a refined TDOA estimation is realized by applying the inverse Fourier transformation on the extracted TF points. Furthermore, a second-order-based time difference reassigned synchrosqueezing transform algorithm is proposed to improve the robustness of the TDOA estimation by enhancing the TF energy aggregation. The proposed algorithms are verified by simulations and experiments. The results show that the proposed algorithms are more robust and accurate than the existing algorithms.

Estimating the effects of roads on migration: a barren-ground caribou case study

A challenge for management of wildlife species is the assessment of the effect of roads on migration. We developed models to estimate the spatial (zone of influence (ZOI)) and temporal (delays in migration) effects of roads, and test whether road closures reduced delays in migration. We analyzed collar (2011–2019) and road survey data from two barren-ground caribou ( Rangifer tarandus groenlandicus (Borowski, 1780)) herds to assess the impact of a 171 km mine road that bisects their migration corridor. We estimated ZOIs of 16–17 km prior to crossing the road during 2018 and 2019, and 3.0 km after crossing. Estimates of mean delay were 4.3 and 2.5 days for 2018 and 2019, which were reduced by 28%–68% (1.2–1.7 days) if roads were closed when caribou were within the ZOI. In 2017–2018, when the road was extended 64 km north, caribou were 12% less likely to deflect north around the road, therefore increasing delays. Road surveys indicated aggregation of caribou prior to crossing the road, with few caribou observed after crossing, a finding supported by collar data. Our methods can estimate the spatial and temporal effects of roads for any wildlife species and assess mitigation strategies in reducing delays in migration.

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

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)

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.

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.

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.