Time-delay estimation in biomechanical stability: a scoping review.

In most of biomedical signal processing applications the presence of interference signals is inevitable. Processing of lung sounds is not an exemption. In this case the heart sounds are unavoidable and sometimes a severe disturbing noise source. Previous studies indicated that the performance of any interference cancellation scheme relies strongly on an adequate time delay estimation between the reference and the primary signals. Here, a new procedure for time delay estimation using the multi-resolution framework is presented. The proposed methodology possesses such key benefits as the multiple looks information of a signal at different bands, robustness in presence of noise and a validation procedure for the delay estimates. The procedure is applicable to a broad range of problems where time delay estimation is required. Results obtained with lung sounds show the promise of the scheme for the time delay estimation and subsequent interference cancellation.

Accurate localization in harsh indoor environments has long been a challenging problem due to the presence of multipath. Since joint direction-of-arrival (DOA) and time delay (TD) estimation has the capability to separate the line-of-sight signal from multipath signals in the TD space, it has recently become a key technique for accurate indoor localization in next-generation WiFi and 5G networks. This paper addresses the problem of joint azimuth, elevation, and TD estimation of multiple reflections of a known signal. First, we propose an efficient approximate maximum likelihood algorithm for this problem, which updates the DOA and TD parameters alternatingly. This algorithm applies to arbitrarily distributed (planar or 3-D) arrays. Then, we present the closed-form Cramer–Rao bound for joint DOA and TD estimation, based on which we provide further analysis to show the benefit of joint DOA and TD estimation over DOA-only estimation. Although the benefit of joint estimation has been empirically shown long ago, our analysis is the first theoretical proof of it. Finally, simulation results have been provided to demonstrate the theoretical finding and the effectiveness of the new algorithm. Matlab code for the new algorithm is available at https://github.com/FWen/JADE.git .

The use of active vibration control may induce a delay leading to detrimental degradation of the performance of active vibration control. This is particularly true in the case of mechanical systems subjected to friction-induced vibration and noise for which such time-delays can lead to the appearance of undesirable instability. Furthermore, conducting a stability analysis of time-delay systems and estimation of the critical time delay are challenging, due to the infinite nature of the characteristic (quasi) polynomial of the associated closed-loop system, having an infinite number of roots.The objective of this paper is to discuss a strategy for the estimation of the critical time delay for the problem of Friction-Induced Vibration and noisE (FIVE). To achieve such an objective, the prediction of the stability analysis of time delay systems and the estimation of the associated critical time delay are first performed by applying the frequency sweep test and the eigenvalue problem approximation using the Taylor series expansion of the delayed term. In a second time, a mixed approach is proposed to predict effectively the real critical time delay of autonomous controlled systems subjected to friction-induced vibration. The efficiency of the proposed approach is illustrated by numerical examples for the prediction of self-sustaining vibrations of a phenomenological model with two degrees of freedom for which it is possible to provide a clear understanding and illustration of the phenomena involved and observed.

Supply of uninterrupted power is of present need in progressing India. The most of these interruptions occur mainly due to failures in insulations of electrical apparatus. Therefore, the present work aimed at developing an early warning system for electrical distribution substations by predicting insulation breakdown. This paper, focused on time delay estimation in between the UHF signals received at multiple sensors placed around the substation. The delay estimation is most critical and essential part in detection and localization of electrical component having weakness or some defects in insulation and act as PD source. Unfortunately, the radiated UHF signals are highly contaminated with noise, form mobile, TV, and Radio broadcastings. Previous works on time delay estimation in the area mainly depends on time domain based estimation procedures, such as first peak (FP), cumulative energy (CE), and cross correlation (CC). However, recent study reveals that, each PD sources having less variant and almost unique frequency domain based signature. Therefore, in the work, Generalized Cross Correlation (GCC) which is a frequency domain based correlation technique for estimation of time delay has been investigated in details. In the work different weighing functions for the GCC, such as Smoothed Coherence Transform (SCOT), the Phase Transform (PHAT), the Eckart Filter (EF) and the Maximum Likelihood (ML) estimator. The study reveals that the GCC with ML function provide most accurate and precise estimation of time delay.

A time delay estimation-based PD-sliding mode control method is proposed to implement high performance trajectory tracking control for a hybrid robot used for automobile electro-coating conveying. First, to exempt the difficulty of real-time dynamic control from calculations of hybrid robot dynamics equations due to its complex mechanism, the time delay estimation-based dynamic model is established by linearizing the dynamic model with time delay estimation technology. Then, a sliding mode control method for the hybrid robot based on time delay estimation-based linearized dynamic model is presented for solving the problem of time delay estimation errors and overcoming the external interferences dramatically. Moreover, the calculation on system dynamics of sliding mode control for the hybrid robot is avoided by substituting the PD control item for the equivalent item of sliding mode control. Lyapunov stability theorem is used to prove the stability of the proposed control law. Finally, the PD control and the proposed control method for the hybrid robot are simulated by MATLAB, and the experiment is carried out on the prototype. The results demonstrate that the proposed control method is independent of complex dynamic model which could avoid the calculation on system dynamics of sliding mode control for the hybrid robot, and it could realize high-accuracy trajectory tracking control compared to PD control under external disturbances.KeywordsHybrid robotPD controlSliding mode controlTime delay estimation

The problem of forward and backward time delays is significantly important for both control and feedback loop of networked control systems. These time delays give rise to latency in performance and thereby may destabilize the system. Therefore numerous methods have been proposed about time delay identification/estimation and compensation for networked control systems, especially for bilateral teleoperation systems. However, most compensation methods have been accomplished by considering offline time delay estimation for linear/nonlinear time delay control systems. In this work, we propose an observer based estimation algorithm for round trip delay which is the sum of forward and backward time delays for a 1 degree-of-freedom nonlinear bilateral teleoperation system. Via Lyapunov based stability analysis, global boundedness of the observer errors along with their ultimate convergence and the convergence of the round trip delay estimator to the vicinity of its real value can be guaranteed in the closed-loop system. Finally, simulation and experimental studies are carried out utilizing the last link of a PHANToM Omni Haptic device moving like a one-link robot in the vertical plane.

In this paper we present a highly accurate, spline-based time delay estimator (TDE) that directly determines subsample time delay estimates from sampled data. The algorithm uses cubic splines to produce a continuous time representation of a reference signal and then computes an analytical matching function between this reference and a delayed signal. The location of the minima of this function yields estimates of the time delay. We also present a more computationally efficient formulation of this algorithm, which is based on FIR filtering to determine the cubic spline coefficients, polynomial approximation to determine the time delay estimates and adaptive search of the delay estimate. The proposed algorithm is particularly useful in applications such as blood flow estimation and tissue elasticity estimation since it can include companding with little additional computational cost.

Relative navigation of spacecrafts may be accomplished by observing X-ray sources and indirectly determining the spacecraftspsila relative position. In this approach, two spacecrafts lock on a known pulsar which irradiates X-ray waveforms that reach them with a differential time delay that is proportional to the distance between the spacecrafts. By observing different pulsar sources geometrically distributed over the galactic disc, it is possible to determine the spacecraftspsila relative inertial position. Our goal is to estimate their relative position by Time Delay Estimation (TDE) between the detected signals. Although there are several off-line TDE methods, like the basic cross-correlation (BCC) and the generalized cross-correlation (GCC) techniques, in this work we formulate TDE as a channel estimation problem and apply adaptive filtering techniques to estimate the time delay online. There are certain benefits in using adaptive filters, especially when the underlying parameters like signalspsila statistics are unknown or change over time. We study different adaptive algorithms and show how they are able to efficiently deliver accurate delay estimates at reduced computational complexity and in real time.

Magnitude squared coherence (MSC) and time delay are two important quantities needed for passive detection and localization of a radiating source using several sensors. This paper presents a novel approach to estimating the MSC and time delay from the outputs of two sensors. It first models the MSC as the product of the transfer functions of two auto-regressive moving-average (ARMA) filters. The ARMA coefficients are then determined by a least squares, matrix pseudoinverse solution to the estimation equation. Time delay estimation follows as a by-product since both phase and MSC estimates are now available. Simulation results obtained from this modeling approach are compared against those estimated from the traditional periodograms. In the majority of cases studied, the ARMA approach appears to be superior, giving smaller bias and variances in both MSC and time delay estimations.

In multiuser MIMO uplink, different pairs of transmitter and receiver may have different time delays and carrier frequency offsets (CFOs). This causes the time delay and CFO estimations much more difficult than those for a single input single output (SISO) system. It is a challenging problem to resolve the time delays and CFOs in the MIMO uplink. In this paper, an asynchronous multiuser MIMO system with multipath channels and CFOs is remodeled into another system. A blind method based on second order statistics is then used for estimating the shaped channels with ambiguity. Based on the shaped channels, specially designed pilots are proposed to find the CFOs. The same pilots are also used for resolving the time delays and the ambiguity in the estimated channels. Only upper bounds for the channel orders and time delays are needed for implementing the algorithms, which makes them applicable to practical multiuser MIMO systems.

Cosmological parameters encoding our understanding of the expansion history of the universe can be constrained by the accurate estimation of time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cosmological time delays by modeling observed and irregularly sampled light curves as realizations of a continuous auto-regressive moving average (CARMA) process. Our model accounts for heteroskedastic measurement errors and microlensing, an additional source of independent extrinsic long-term variability in the source brightness. The semiseparable structure of the CARMA covariance matrix allows for fast and scalable likelihood computation using Gaussian process modeling. We obtain a sample from the joint posterior distribution of the model parameters using a nested sampling approach. This allows for “painless” Bayesian computation, dealing with the expected multimodality of the posterior distribution in a straightforward manner and not requiring the specification of starting values or an initial guess for the time delay, unlike existing methods. In addition, the proposed sampling procedure automatically evaluates the Bayesian evidence, allowing us to perform principled Bayesian model selection. TD-CARMA is parsimonious, and typically includes no more than a dozen unknown parameters. We apply TD-CARMA to six doubly lensed quasars HS2209+1914, SDSS J1001+5027, SDSS J1206+4332, SDSS J1515+1511, SDSS J1455+1447, and SDSS J1349+1227, estimating their time delays as −21.96 ± 1.448, 120.93 ± 1.015, 111.51 ± 1.452, 210.80 ± 2.18, 45.36 ± 1.93, and 432.05 ± 1.950, respectively. These estimates are consistent with those derived in the relevant literature, but are typically two to four times more precise.

Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.

Noise reduction in elastograms using temporal stretching with multicompression averaging

A novel approach for estimating constant time delay through the use of neural networks (NN) is introduced. A desired reference signal and a delayed, damped, and noisy replica of it are both filtered by a fourth-order digital infinite impulse response (IIR) filter. The filtered signals are normalized with respect to the highest values they achieve and then applied as input for an NN system. The output of the NN is the estimated time delay. The network is first trained with one thousand training data set in which each data set corresponds to a randomly chosen constant time delay. The estimated time delay obtained by the NN is an accurate estimate of the exact time-delay values. Even in the case of noisy data, the estimation error obtained was a fraction of the sampling time interval. The delay estimates obtained by the NN are comparable to the estimated delay values obtained by the cross-correlation technique. The main advantage of using this technique is that accurate estimation of time delay results from performing one pass of the filtered and normalized data through the NN. This estimation process is fast when compared to the classical techniques utilized for time-delay estimation. Classical techniques rely on generating the computationally demanding cross-correlation function of the two signals. Then a peak detector algorithm is utilized to find the time at which the peak occurs.

Partial discharge (PD) localization in substations based on the ultra-high frequency (UHF) method can be used to efficiently assess insulation conditions. Localization accuracy is affected by the accuracy of the time delay (TD) estimation, which is critical for PD localization in substations. A review of existing TD estimation methods indicates that there is a need to develop methods that are both accurate and computationally efficient. In this paper, a novel TD estimation method is proposed to improve both accuracy and efficiency. The TD is calculated using an improved cross-correlation algorithm based on full-wavefronts of array UHF signals, which are extracted using the minimum cumulative energy method and zero-crossing points searching methods. The cross-correlation algorithm effectively suppresses the TD error caused by differences between full-wavefronts. To verify the method, a simulated PD source test in a laboratory and a field test in a 220 kV substation were carried out. The results show that the proposed method is accurate even in case of low signal-to-noise ratio, but with greatly improved computational efficiency.