Time Delay Estimation Research Articles

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer’s disease

BackgroundThe clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer’s disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes.MethodsWe simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale–sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years.ResultsThe PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease.ConclusionPMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Trajectory Following Control of an Unmanned Vehicle for Marine Environment Sensing.

An autonomous surface vehicle is indispensable for sensing of marine environments owing to its challenging and dynamic conditions. To accomplish this task, the vehicle has to navigate through a desired trajectory. However, due to the complexity and dynamic nature of a marine environment affected by factors such as ocean currents, waves, and wind, a robust controller is of paramount importance for maintaining the vehicle along the desired trajectory by minimizing the trajectory error. To this end, in this study, we propose a robust discrete-time super-twisting second-order sliding mode controller (DSTA). Besides, this control method effectively suppresses the chattering effect. To start with, the vehicle's model is discretized using an integral approximation with nonlinear terms including environmental disturbances treated as perturbation terms. Then, the perturbation is estimated using a time delay estimator (TDE), which further enhances the robustness of the proposed method and allows us to choose smaller controller gains. Moreover, we employ a genetic algorithm (GA) to tune the controller gains based on a quadratic cost function that considers the tracking error and control energy. The stability of the proposed sliding mode controller (SMC) is rigorously demonstrated using a Lyapunov approach. The controller is implemented using the Simulink® software. Finally, a conventional discrete-time SMC based on the reaching law (DSMR) and a heuristically tuned DSTA controller are used as benchmarks to compare the tracking accuracy and chattering attenuation capability of the proposed GA based DSTA (GA-DSTA). Simulation results are presented both with or without external disturbances. The simulation results demonstrate that the proposed controller drives the vehicle along the desired trajectory successfully and outperforms the other two controllers.

Variable time delay estimation in continuous industrial processes

Digital sensors and machine learning enable efficiency improvements in production processes, through process monitoring, anomaly detection, soft sensing, and process control. However, the development of such solutions requires several data preprocessing steps. In continuous processes, a crucial part of the data preparation is adjusting for time delays between different sensors. This is necessary to ensure that each sensor measurement relate to the same volume of materials going through various processing steps.This study provides an overview of data-driven methods for estimating time lags between sensors in continuous processes. The methods are assessed in a large simulation study, on data sets with different sample sizes, model complexities and autocorrelation functions. Our results shows that most methods work well if the relationships are close to linear, but more flexible metrics like distance correlation and maximum information coefficient are needed in more complex systems. Finally, we present a real industrial example to illustrate some real-world aspects of the variable time delay estimation process.

Finite-Time Robust Flight Control of Logistic Unmanned Aerial Vehicles Using a Time-Delay Estimation Technique

This paper proposes a cascaded dual closed-loop control strategy that incorporates time delay estimation and sliding mode control (SMC) to address the issue of uncertain disturbances in logistic unmanned aerial vehicles (UAVs) caused by ground effects, crosswind disturbances, and payloads. The control strategy comprises a position loop and an attitude loop. The position loop, which functions as the outer loop, employs a proportional–integral–derivative (PID) sliding mode surface to eliminate steady-state error through an integral component. Conversely, the attitude loop, serving as the inner loop, utilizes a fast nonsingular terminal sliding mode approach to achieve finite-time convergence and ensure a quick system response. The time-delay estimation technique is employed for the online estimation and real-time compensation of unknown disturbances, while SMC is used to enhance the robustness of the control system. The combination of time-delay estimation and SMC offers complementary advantages. The stability of the system is proven using Lyapunov theory. Hardware-in-the-loop simulation and flight tests demonstrate that the control law can achieve a smooth and continuous output. The proposed control strategy can be effectively applied in complex scenarios, such as hovering, crash recovery, and high maneuverability flying, with significant practicality in engineering applications.

Multispacecraft Energetic Particle Enhancements Associated with a Single Corotating Interaction Region

The radial evolution of particles accelerated at corotating interaction regions (CIRs) is not fully understood, particularly the distance range over which this particle acceleration occurs and how the energy spectra are modulated by transport through the inner heliosphere. Here, we present observations of energetic proton enhancements associated with a CIR observed by Parker Solar Probe on 2021 April 25 during the inbound leg of its orbit near ∼46 R s (∼0.21 au). The CIR is identified at additional spacecraft (Solar Terrestrial Relations Observatory, STEREO-A; Solar Orbiter, SolO; and Advanced Composition Explorer, ACE) using a corotation time delay estimation, and energetic proton spectra from each spacecraft are compared. We find that (1) energetic protons are observed near 46 R s streaming sunward ahead of the CIR; (2) the CIR persists for at least one solar rotation and the corresponding energetic proton enhancements are observed at STEREO-A, SolO, and ACE; and (3) the proton energy spectrum is steeper near the Sun and hardens near 1 au. This observation presents the closest in situ energetic particle observation of a CIR to the Sun ever recorded. Results presented here suggest that particles can be accelerated by CIR structures within 1 au and these particles can penetrate very deep into the inner heliosphere.

A Variable Fractional Delay Filter Based True-Time-Delay Array Architecture for Wideband Beamforming

True-time delay (TTD) units are critical in Wideband Large-Scale Antenna Systems (LSASs) for resolving beam squint and frequency selective fading. In this paper, we propose a TTD array architecture for wideband beamforming that addresses the challenges posed by LSASs by utilizing variable fractional delay (VFD) filters and time delay estimations (TDEs). This study introduces a convex optimization problem to develop a novel method for designing VFD filters that can achieve high precision time delay resolution while maintaining an acceptable frequency response peak error. Using these VFD filters, we present a TDE approach with Newton-Raphson iteration update that corrects the difference in arrival time between sensors. Due to the implementation of VFD filters and fast TDE modules, the new architecture can complete beam steering and tracking in 10 ms with MATLAB simulation. Furthermore, an example was designed to demonstrate that the proposed architecture is capable of producing precise beam pointing while maintaining the bit error rate (BER) that is extremely close to the theoretical curve over the entire bandwidth.

Model-free scheme using time delay estimation with fixed-time FSMC for the nonlinear robot dynamics

<abstract><p>This paper presents a scheme of time-delay estimation (TDE) for unknown nonlinear robotic systems with uncertainty and external disturbances that utilizes fractional-order fixed-time sliding mode control (TDEFxFSMC). First, a detailed explanation and design concept of fractional-order fixed-time sliding mode control (FxFSMC) are provided. High performance tracking positions, non-chatter control inputs, and nonsingular fixed-time control are all realized with the FxSMC method. The proposed approach performs better and obtains superior performance when FxSMC is paired with fractional-order control. Furthermore, a TDE scheme is included in the suggested strategy to estimate the unknown nonlinear dynamics. Afterward, the suggested system's capacity to reach stability in fixed time is determined by using Lyapunov analyses. By showing the outcomes of the proposed technique applied to nonlinear robot dynamics, the efficacy of the recommended method is assessed, illustrated, and compared with the existing control scheme.</p></abstract>

Joint Time Delay and Frequency Estimation Based on Deep Learning

Joint Time Delay and Frequency Estimation Based on Deep Learning

Analysis and Preliminary Results of a Feedback-Feedforward Controller for Depth of Anesthesia

In a computer-based anaesthesia control system, the depth of hypnosis must be correctly maintained. The regulated variable is the Bispectral Index scale (BIS) controlled via the Propofol drug rate. The anesthesiologist administers Propofol to prevent unwanted fluctuations in the controlled variable during surgical stimulation, which acts as an unmeasurable disturbance on the BIS signal. In this paper, a feedback controller is paired with a feedforward control action. Based on existing clinical protocols, a digitalized disturbance signal that mimics the actual surgical stimulus is employed in the feedforward controller design as a measurable disturbance. To account for the time delay in the BIS measurements, a time delay estimation algorithm is developed and used to estimate the patient time delay. The digitalized disturbance signal is then shifted based on the estimated time delay to ensure a pre-emptive action of the feedforward controller. Two different designs of the feedforward controller are developed. Closed loop simulations considering a nominal patient with and without the feedforward control action are presented and compared. The results show that the surgical stimulus is better tackled using a feedforward control action, even if the actual surgical stimulus is not entirely known.

Optimization of Time Delay Estimation by Singular Spectral Decomposition and Improved Generalized Correlation Method

Optimization of Time Delay Estimation by Singular Spectral Decomposition and Improved Generalized Correlation Method

Interferometric Distributed Fiber Optic Vibration Sensor with Branching Sensing Optical Path Based on Time Delay Estimation for Disturbance Localization

Interferometric Distributed Fiber Optic Vibration Sensor with Branching Sensing Optical Path Based on Time Delay Estimation for Disturbance Localization

Time-Delay Estimation Based on an Enhanced Modified MUSIC With Co-Prime Frequency Sampling for Rough Pavement

Time-Delay Estimation Based on an Enhanced Modified MUSIC With Co-Prime Frequency Sampling for Rough Pavement

Co-Prime Sampling-Based Time-Delay Estimation for Roadway Survey by Ground Penetrating Radar via Off-Grid Sparse Bayesian Learning

Co-Prime Sampling-Based Time-Delay Estimation for Roadway Survey by Ground Penetrating Radar via Off-Grid Sparse Bayesian Learning

Time-Delay Estimation of Ground-Penetrating Radar Using Co-Prime Sampling Strategy via Atomic Norm Minimization

Time-Delay Estimation of Ground-Penetrating Radar Using Co-Prime Sampling Strategy via Atomic Norm Minimization

Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography.

Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first-order displacement derivative into account; (2) The -norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of -norm. Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms. Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of -norm data fidelity term and -norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth backgrounds and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and -SOUL, three recently published energy-based techniques, and UMEN-Net, a state-of-the-art deep learning-based algorithm. Analysis of variance (ANOVA)-led multiple comparison tests and paired -tests at overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard-inclusion simulated phantom, one multi-inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at http://code.sonography.ai. ALTRUIST substantially outperforms the four state-of-the-art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to , , and SNR improvements and , , and CNR improvements over -SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA-led multiple comparison tests and paired -tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN-Net, OVERWIND, and -SOUL in terms of AE, SSIM map, SNR, andCNR. A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an -norm regularization-based cost function. ALTRUIST exhibits promising performance in simulation, phantom, and in vivoexperiments.

Precise disturbance localization of long distance fiber interferometer vibration senor based on an improved time–frequency variation feature extraction scheme

Locating intrusions with high accuracy is crucial for prompting long distance distributed optical fiber vibration sensing systems to practical applications in engineering filed. To achieve the high measurement accuracy and real-time performance simultaneously, in this paper, an intensity-modulated ultra-long distance distributed optical fiber vibration sensing system using an asymmetric dual Mach-Zehnder interferometer is experimentally established. More importantly, a positioning approach based on an ameliorated time–frequency feature extraction scheme is proposed with both theoretically analysis and experimental demonstration. In particular, an endpoint detection algorithm based on differential operation is firstly utilized to rapidly and accurately locate the endpoints of the received vibration signals. Thus the large-band width signals can be successfully acquired by setting an approximate window. Then, the ameliorated time–frequency feature extraction scheme based on a maximal overlap discrete wavelet packet transform is applied to eliminate the asymmetry of the large-band width signals, as it could lead to an erroneous positioning result when directly using a cross-correlation time delay estimation algorithm. Finally, the vibration positions along the sensing fiber link of the established sensing system can be successfully demodulated with the help of the extracted time–frequency features. To validate the effectiveness of the proposed scheme, vibration positioning tests were conducted in a real perimeter security scenario. And the results show that a mean positioning error of 11.12 m and a mean processing time of 0.276 s have been realized in a sensing length of 101 km. Thus, it is believed that through continuous technological advancements and refine algorithm optimization, the long distance distributed optical fiber vibration sensor founded on the proposed positioning strategy is poised to play an important role in the practical engineering applications.

Time-Delay Control Scheme with Adaptive Fixed-Time Convergent Super-Twisting Fractional-Order Nonsingular Terminal Sliding Mode for Piezoelectric Displacement Amplifier

Piezoelectric displacement amplifiers (PDAs) have been widely used in precision positioning fields. However, the inherent hysteresis and creep nonlinear effect of piezoelectric actuators (PEAs) and time-varying lumped disturbances bring extreme challenges to the precise motion control of PDAs. Although various control schemes based on PEAs have been developed and have shown significant results. However, due to the high sensitivity of precision positioning to environmental variations, the development and identification of accurate models and the control timeliness often become obstacles in engineering. To realize precise motion control of PDAs under complex lumped disturbances, a new time-delay control scheme (AFSTA-FONTSM) using an adaptive fixed-time convergent super-twisting algorithm (AFSTA) and a fractional-order nonsingular terminal sliding mode (FONTSM) is proposed. Specifically, the time-delay information obtained by time-delay estimation technology is used to estimate the lumped dynamic characteristic of the system, thus establishing a simple control framework without a system dynamic model. FONSTM is constructed as a sliding mode manifold, and satisfactory error dynamic characteristic is obtained. A new AFSTA is designed as the reaching law in the sliding mode phase. AFSTA has fixed-time convergence when the upper bound of lumped disturbances exists, which ensures the control timeliness. Benefiting from the newly designed adaptive algorithm, the upper bound value of lumped disturbances is no longer needed to determine the control gains, which effectively prevents overestimation of the control gains. Correspondingly, the convergence time of AFSTA is estimated, and the stability of the closed-loop system is analyzed by the Lyapunov theory. Three existing time-delay control schemes, namely MSTA-FONTSM, AMSTA-FONTSM, and ASTA-FONTSM are selected, and four scenes are designed for comparative experiments. The experimental results show that MSTA-FONTSM has the worst control performance among the four control schemes. For the step, and continuous cosine trajectories with periods of T=1s and T=2s, the root-mean-square error of the proposed AFSTA-FONTSM is reduced by 56.86%, 54.03%, and 50.24% compared with MSTA-FONTSM. For disturbance experiments under different loads, the control performance of the proposed AFSTA-FONTSM is still superior to the other three control schemes without load.

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%.

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.

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.