Accurate Delay Estimation Research Articles

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved GCC-PHAT Weighting.

The accuracy of time delay estimation seriously affects the accuracy of sound source localization. In order to improve the accuracy of time delay estimation under the condition of low SNR, a delay estimation optimization algorithm based on singular value decomposition and improved GCC-PHAT weighting (GCC-PHAT- weighting) is proposed. Firstly, the acoustic signal collected by the acoustic sensor array is subjected to singular value decomposition and noise reduction processing to improve the signal-to-noise ratio of the signal; then, the cross-correlation operation is performed, and the cross-correlation function is processed by the GCC-PHAT- weighting method to obtain the cross-power spectrum; finally, the inverse transformation is performed to obtain the generalized correlation time domain function, and the peak detection is performed to obtain the delay difference. The experiment was carried out in a large outdoor pool, and the experimental data were processed to compare the time delay estimation performance of three methods: GCC-PHAT weighting, SVD-GCC-PHAT weighting (meaning: GCC-PHAT weighting based on singular value decomposition) and SVD-GCC-PHAT- weighting (meaning: GCC-PHAT- weighting based on singular value decomposition). The results show that the delay estimation optimization algorithm based on SVD-GCC-PHAT- improves the delay estimation accuracy by at least 37.95% compared with the other two methods. The new optimization algorithm has good delay estimation performance.

Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections

The Highway Capacity Manual (HCM) 2016 recommends using right-turn-on-red (RTOR) volume for capacity and level of service analyses but provides very limited guidance for predicting the volume in the absence of field data. The signalized intersection methodology in the HCM computes capacity using only the green portion of the cycle, ignoring the additional capacity that may result from allowing RTOR. This leads to an overestimation of delay, and consequently a determination of level of service that is likely to appear worse than the actual performance. Therefore, it is essential to develop proper RTOR volume and capacity estimation models for correct estimation of the level of service at signalized intersections. In this study, regression models for RTOR volume were developed for intersections where the subject RTOR movement occurs from an exclusive right-turn lane. Data from 128 intersections across the United States was used for the analysis. Four different categories of regression model with three types of model under each category were developed and their performances were compared using a testing data set. Tobit regression performed better than other types of regression in predicting the RTOR volume. The model will be useful in predicting RTOR volumes in the absence of field data for accurate estimation of delay and level of service at a signalized intersection.

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type.

Noise Reduction Based on CEEMDAN-ICA and Cross-Spectral Analysis for Leak Location in Water-Supply Pipelines

Due to the leakage vibration signal of the water-supply pipeline is easily interfered with by background noise, which leads to a significant leakage location error of time delay estimation. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) combined noise reduction method based on cross-spectral analysis is proposed and applied to leak location of water supply pipelines. Firstly, determine the frequency band range of the source leakage signal by cross-spectral analysis of the leakage vibration signal. At the same time, the leakage vibration signal is decomposed into a finite number of intrinsic mode functions (IMFs) using CEEMDAN. Then, the IMF components are filtered according to the effective amplitude ratio criterion, and the filtered components are reconstructed into a virtual noise channel. Finally, the leakage vibration signal and virtual noise channel are unmixed using FastICA to obtain the best estimate of the source leakage signal, which is used to estimate time delay using cross-correlation for determining the leak location. Simulation and experimental results show that the proposed method effectively improves the signal-to-noise ratio (SNR) and time delay estimation accuracy, and reduces leak location error. Compared with cross-correlation, CEEMDAN-ICA based on correlation coefficients, and CEEMDAN based on cross-spectral analysis, the average relative positioning error of the proposed method is respectively reduced by 11.07%, 9.7%, and 1.8%. In addition, the superiority of the proposed method compared with other state-of-the-art noise reduction methods is verified by comparative experiments.

Accurate Delay Estimation for Multisensor Passive Locating Systems Exploiting the Cross-Correlation Between Signals Cross-Correlations

The availability of accurate estimates of the delay or time of arrival of the incoming signals is of paramount importance for the position estimation in passive radars with multiple receivers. This correspondence aims at improving estimation of the delays by multiple detectors exploiting the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The resulting equation system is formulated as a least squares minimization problem, whose solution is efficiently found computing the pseudoinverse of the model matrix. In fact, the cross-cross-correlation implicitly performs a filtering operation on the considered signal, approximating the generalized cross-correlator behavior, without using statistical information about the signal spectra. The proposed method is numerically validated in comparison with classic counterparts and theoretical bounds.

An LBL positioning algorithm based on an EMD\u2013ML hybrid method

Autonomous underwater vehicles (AUVs) are essential assets for ocean exploration requiring reliable underwater positioning technology. Aiming to improve the latter technology in a low SNR and reverberation environment, the Chan–Taylor hybrid positioning algorithm establishes a long-baseline system (LBL) based on the time difference of arrival (TDOA) by reducing the number of sensors required while preserving the positioning accuracy. However, the traditional LBL algorithm’s accuracy is reduced due to the critical time delay estimation under such environmental conditions. Hence, this paper suggests a new LBL positioning technology relying on an empirical mode decomposition (EMD) to construct a filter function combined with the maximum likelihood (ML) estimation method. MATLAB/Simulink is applied to establish the simulation environment of LBL localization system, simulating the AUV motion under 5–30 dB SNR. This paper analyzes the accuracy of TDOA by generalizing the cross-correlation method (GCC), phase transform (PHAT), ML, and EMD–ML. Based on the TDOA value obtained by the EMD–ML filtering algorithm, the positioning errors of the Chan–Taylor hybrid positioning algorithm and the Chan algorithm are compared. The results show that comparative synthetic evaluations against the traditional GCC demonstrate that the proposed method has a higher time delay estimation accuracy within a reverberation environment with SNR less than 15 dB. The Chan–Taylor hybrid positioning algorithm limits the errors of the CHAN algorithm and improves the overall positioning system accuracy.

Forecasting tropospheric wet delay using LSTM neural network

Tropospheric wet delay is a critical factor in radio communication. Accurate estimation of the wet delay is difficult due to the variability in water vapour. In this study, we aim to model and predict tropospheric wet delay over four tropical locations using Long Short-Term Memory (LSTM) neural network. Results obtained in this study showed RMSE and MAE within the range 18.96–21.16 and 14.08–16.38 respectively. LSTM model was able to capture the different regimes of wet delays in each of the locations under consideration. This approach can significantly improve link budget and planning within tropical regions.

Second-order Iterative Time-rearrangement Synchrosqueezing Transform and its application to rolling bearing fault diagnosis

Rolling bearings are key components in rotating machines, and their health condition is an important guarantee for the safety of the whole machine. In order to extract fault features of rolling bearings more accurately, a novel signal processing method named Second-order Iterative Time-rearrangement Synchrosqueezing Transform (2-ITSST) with a fast algorithm is proposed in this paper. Firstly, based on Time-reassigned SynchroSqueezing Transform (TSST), the approximation order is increased from the first order to the second order. Then, multiple iterations are conducted to calculate a more accurate group delay (GD) estimation operator. After that, the rearrangement operation is carried out to maximize the concentration of energy near the time-frequency (TF) ridgeline and obtain the result with higher TF aggregation. Lastly, the fast algorithm for 2-ITSST is introduced to overlap TF matrices into segments, which effectively reduced the computational complexity and ensured the TF aggregation of impact signal energy. Meanwhile, in order to make 2-ITSST more efficiently applied in bearing fault diagnosis, impulse extraction method is introduced to extract useful impulse features from the signal processed by 2-ITSST algorithm, and then the bearing fault frequency can be accurately extracted by envelope spectrum analysis. The effectiveness of the proposed method is verified by simulation and experimental studies.

A Closed-form Delay Estimation Model for Current-mode High Speed VLSI Interconnects

Closed-form model for the delay estimation of current-mode Resistance Inductance Capacitance (RLC) interconnects in VLSI circuits is presented. The existing Eudes model for interconnect transfer function approximation is extended and applied for further accurate estimation of delay. With the equivalent lossy interconnect transfer function, finite ramp responses are obtained and line delay is estimated for various line lengths, per unit length inductances and load capacitances. The estimated delay values of extended Eudes model are compared with the existing Eudes model against HSPICE W-element model. The obtained delay values of Eudes model worst-case error percentage is 14.3% whereas our extended Eudes model is in good agreement with those of HSPICE results within 2% for the line lengths of 1mm to 10mm.

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

Accurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), such delay estimation should not be limited to a maximum value defined in the Service Level Agreement, but to a finer-grained description of the expected delay in the form of, e.g., a continuous function of the load. Obtaining accurate end-to-end (e2e) delay modeling is even more challenging in a multi-operator (Multi-AS) scenario, where the provisioning of e2e connectivity services is provided across heterogeneous multi-operator (Multi-AS or just domains) networks. In this work, we propose a collaborative environment, where each domain Software Defined Networking (SDN) controller models intra-domain delay components of inter-domain paths and share those models with a broker system providing the e2e connectivity services. The broker, in turn, models the delay of inter-domain links based on e2e monitoring and the received intra-domain models. Exhaustive simulation results show that composing e2e models as the summation of intra-domain network and inter-domain link delay models provides many benefits and increasing performance over the models obtained from e2e measurements.

Gate Delay Estimation With Library Compatible Current Source Models and Effective Capacitance

As process geometries shrink below 45 nm, accurate and efficient gate-level timing analysis becomes even more challenging. Modern VLSI interconnects are more resistive, signals no longer resemble saturated ramps, and gate input pins exhibit a significant Miller effect. Over recent years, the semiconductor industry has adopted current source models (CSMs) for accurate gate modeling. Industrial gate models, however, are precharacterized assuming capacitive loads, which poses significant challenges to the approximation of the highly resistive load interconnect with an effective capacitance ( C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</sub> ). In fact, most related works are either computationally expensive or unable to approximate the output slew. Furthermore, they require additional precharacterization and ignore the Miller effect. In this article, we present an iterative methodology for fast and accurate gate delay estimation. The proposed approach accurately computes the driver output waveform, using closed-form formulas to calculate a C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</sub> per waveform segment, while accounting for their interdependence. Thus, it allows for variable analysis resolution exploiting an accuracy/runtime tradeoff. In contrast to prior works, our approach is compatible with conventional CSMs and considers the impact of Miller capacitance. We evaluate our method on representative driver-load test circuits consisting of interconnects with arbitrary RC characteristics and ASU ASAP 7-nm standard cells. The proposed method achieves 1.3% and 2.5% delay and slew root-mean-square percentage error (RMSPE) against SPICE, respectively. In addition, it provides high efficiency, as it converges in 2.3 iterations on average.

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.

Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Deep Learning (DL). DL is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, DL is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Levenberg-Marquart algorithm is applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results. In order to study effect of stack denoising autoencoder and LM algorithm on the model structure, two other structures are also designed. First structure is based on autoencoder and LM algorithm (SAE-LM), and the second structure is based on denoising autoencoder only (SDA). To investigate the three models, we apply the proposed model on U.S flight dataset that it is imbalanced dataset. In order to create balance dataset, undersampling method are used. We measured precision, accuracy, sensitivity, recall and F-measure of the three models on two cases. Accuracy of the proposed prediction model analyzed and compared to previous prediction method. results of three models on both imbalanced and balanced datasets shows that precision, accuracy, sensitivity, recall and F-measure of SDA-LM model with imbalanced and balanced dataset is improvement than SAE-LM and SDA models. The results also show that accuracy of the proposed model in forecasting flight delay on imbalanced and balanced dataset respectively has greater than previous model called RNN.

Cooperative inter-satellite ranging using custom code division multiple access frame for space traffic management

Space traffic is considered a complex contemporary issue originated from the difficulty inherited in the nature of space missions. Satellites and space objects are actively tracked using Earth-based surveillance networks or in-situ sensors like the GPS. The data collected from these Earth-based surveillance networks albeit their public availability, they remain only valuable for trajectory and state verification due to their limited positional accuracy. This is exhibited in the poor accuracy (within 2–5 km) of public two-line elements (TLEs), which worsen when propagated in the future to more than tens of kilometers. Consequently, precision orbit data obtained from onboard GPS systems, are commonly used to alleviate, to a degree, the uncertainty regarding space conjunctions. However, the lack of GPS data standardization and sharing regarding critical satellite conjunctions together with the limited deployment of the GPS system onboard nano-satellites and its susceptibility to Doppler shifts in low Earth orbits, inhibits the full exploitation of the GPS system in space traffic.Targeting instant inter-satellite ranging for improving space situational awareness (SSA), this research explores the benefits of the proposed custom signature sequence to optimize a signal acquisition and detection for coherent and asynchronous synchronisation. This comes with the advantage of accurate propagation delay estimation and bias errors correction. Data sharing also becomes possible using the proposed custom direct sequence code division multiple access (DS-CDMA) and Reed-Solomon forward error correction for a future-proof inter-satellite ranging and data sharing solution. Coherent detection, acquisition and synchronisation are proposed based on data-assisted correlation which reduces the false- and miss-alarms respectively by 27% and 97%. This CDMA frame is based on orthogonal dual-channel separation which offers considerable noise and interference resilience for data recovery, in addition to precise frequency and timing errors correction during synchronisation. Results show that the ranging accuracy approaches meter-level at no expense of increased bandwidth beyond 2 MHz. Occupying the same bandwidth, the ranging resolution scales linearly with the spreading factor contributed by the Kronecker filter length. Additionally, the delay measurement solution is proven unsusceptible to the Doppler frequency errors caused by the high relative velocity between the satellites and Doppler errors within 16 kHz were corrected. Further, the coding gain increases by 16 dB compared to narrow-band communications, using CCSDS Reed-Solomon encoder coupled with data sequence spreading, offering considerable data transfer resilience in low signal-to-noise conditions.

The measurement of Lamb wave phase velocity using analytic cross-correlation method

This paper is devoted to a method for the measurement of Lamb wave phase velocities when the material properties and the thickness of the structure are unknown. In that method, the key of phase-velocity dispersion determination is to achieve an accurate estimation of the group delay and the phase shift at each frequency point. Firstly, a typical chirp excitation is utilized, providing Lamb wave responses at a wide range of frequencies and with a high signal-to-noise ratio. Thus a series of narrowband responses at multiple frequencies could be obtained from a single testing. Subsequently, the analytical cross-correlation method is applied to determine the group delay and phase shift, which only requires measurements at two adjacent positions. On this basis, the phase velocity value at each frequency is calculated from the relations between the propagation distance, group delay and phase shift. The performance of the proposed method is analysed by simulated signals, and validated using experimental signals from an aluminum plate and a carbon fiber-reinforced polymer (CFRP) laminate. Those demonstrate the main advantage of the proposed method that it is quite efficient and simple on both testing and processing.

Efficient interpolation based OMP for sparse channel estimation in underwater acoustic OFDM

To achieve higher accuracy of path delay estimation with lower computational complexity, the orthogonal matching pursuit (OMP) method with interpolation algorithm has been adopted for the sparse channel estimation, such as underwater acoustic (UWA) communication channels. For an orthogonal frequency division multiplexing (OFDM) system with uniform pilots, estimating the path delay by OMP in each its iteration is equivalent to the frequency estimation of single-tone signals with discrete Fourier transform (DFT). In this paper, based on the existing frequency estimation algorithms, we propose two novel interpolation based OMP methods for baseband sampling grid and over-sampling grid respectively, aiming at improving path delay estimation efficiency. Moreover, the closed-form of Cramer-Rao lower bound (CRLB) for single path delay estimation is derived to serve as a benchmark for simulations. The performances of the two proposed methods are verified by both simulations and UWA communication experiment in Yellow Sea, China, which show a higher estimation accuracy than that of the traditional OMP method, and the similar estimation performance but lower computational complexity than that of the existing high-accuracy interpolation based OMP method.

Assessment of errors in NavIC observables for stationary receivers

Our objective in this paper is to assess all errors in NavIC's pseudorange so that they can be mitigated or accounted for appropriately in the dynamics model to achieve high-accuracy navigation. Multipath errors in the L5 and S1 pseudoranges are different; hence, oscillatory multipath is removed using sidereal repeatability for these frequencies separately for accurate iono delay estimation. Iono delay estimates using uncompensated pseudoranges are 7-m greater than the normal night time estimates due to multipath. When oscillations are removed, the difference reduces to =3 m. Dual-frequency iono delay estimates using code phase are within two-sigma of the grid-based estimates. A comparison with the NTCM-GL and Klobuchar iono delay estimates is presented for the low-latitude region in India. The ephemeris line-of-sight errors blended with the unknown multipath bias errors are estimated using a low-pass filter and are found to lie at the edges of the two-sigma signal-in-space error.

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

An inland Wireless Sensor Network system for monitoring seismic activity

Efficient early-warning systems for earthquakes should be able to provide accurate information on the seismic event allowing ample time for precautionary measures. In this paper, an assessment of the feasibility of using a Wireless Sensor Networks (WSNs) for monitoring seismic activity on the island of Mauritius using primary waves (P-waves) is carried out. Mauritius is one of the countries which are highly at risk to natural calamities. A simulation platform is designed and implemented to test the efficiency of a WSN in monitoring seismic activity. The system estimates the location of the hypocenter and the local velocity using time delays in P-wave arrival at each sensing node. Deviations between the estimated and actual quantities are used to study the efficiency of the system. Cross-correlation analysis of the recorded P-wave signals allowed accurate propagation delay estimation up to signal-to-noise ratios (SNRs) as low as -15dB. Time synchronization between the sensing nodes using timing-sync protocol for sensor networks improved the performance of the relocation algorithm by reducing the nodes’ clock offsets to within 0.50ms. The proposed system was tested through numerical simulations with various SNRs, sampling frequencies and number of sensing nodes.

Low probability of intercept‐based OFDM radar waveform design for target time delay estimation in a cooperative radar‐communications system

In this study, a low probability of intercept (LPI)-based orthogonal frequency division multiplexing radar waveform design strategy is developed for target time delay estimation in a cooperative radar-communications system, where the communications base station (CBS) is able to receive and process the radar emissions. The basis of the LPI-based radar waveform design strategy is to utilise the optimisation technique to design the transmitted waveform of a radar system for specified target time delay estimation accuracy in order to achieve enhanced LPI performance. The expression for Cramer–Rao lower bound is analytically derived and utilised as a performance metric for target time delay estimation performance. The resulting optimisation problem is solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar waveform design algorithm in terms of the LPI performance of the radar system. It is also shown that the radar transmitted power can be significantly reduced with the help from the CBS which plays a role of the bistatic radar receiver.