Robust Time Delay Estimation Research Articles

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.

Robust time delay estimation with unknown cyclic frequency in co-channel interference and impulsive noise

Time delay estimation (TDE) has been an essential topic in both theoretical research and engineering practice. The effective models and solutions related to this work have been offered by pioneers, and preferable results are achieved. Nevertheless, in the environment of co-channel interference and impulsive noise, the traditional methods are degraded or even be unavailable. To solve these problems, a robust TDE method is proposed in this paper. On the one hand, the novel method designed in cyclostationary signal processing can suppress the co-channel interference effectively, and the cyclic frequencies of interference signals are not necessary. On the other hand, the novel method equipped with a nonlinear function can deal with impulsive noise and performs well compared to the traditional methods in Gaussian noise. In addition, theoretical analysis and comprehensive Monte Carlo simulations demonstrate the superiority of the proposed method compared to the existing competitive methods. It is believed that the robust TDE method could be fully utilized in more engendering fields, and the technologies involved could also be taken into consideration in other research.

A robust time delay estimation method for ultrasonic echo signals and elastography

Modern medicine cannot ignore the significance of elastography in diagnosis and treatment plans. Despite improvements in accuracy and spatial resolution of elastograms, robustness against noise remains a neglected attribute. A method that can perform in a satisfactory manner under noisy conditions may prove useful for various elastography methods. Here, we propose a method based on eigenvalue decomposition (EVD). In this method, the estimated time delay is defined as the index of the maximum element in the eigenvector that corresponds to the minimum eigenvalue in the covariance matrix of the received signal. Moreover, the implementation of the least-squares (LS) solution and the lower-upper (LU) decomposition contributes to improving the speed of computation and the accuracy of the estimation under low signal-to-noise ratio (SNR) conditions. To assess the performance of the proposed algorithm, it is evaluated along with generalized cross-correlation (GCC) and EVD. The simulation results clearly confirm that the jitter variance achieved in the proposed algorithm outperforms GCC and EVD in the proximity of the Cramer-Rau lower band. Moreover, our algorithm provides satisfactory performance in terms of variance and bias against sub-sample delay at low SNRS. According to the experimental results, the calculated values of the elastographic signal-to-noise ratio (SNRe) and the elastographic contrast-to-noise ratio (CNRe) of the proposed algorithm were 16.7 and 20.09, respectively, clearly better than the values of the other two methods. Furthermore, the proposed algorithm offers less execution time (about 30% of GCC), with a computational complexity equal to GCC and better than EVD.

Robust time delay estimation based on non-Gaussian impulsive acoustic channel

Robust time delay estimation based on non-Gaussian impulsive acoustic channel

A robust time-delay estimation and dereverberation algorithm based on the coherence function

A robust time-delay estimation and dereverberation algorithm based on the coherence function

A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions

Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average <i>p</i> norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.

Real-time DSP-based time delay estimation using two sensors

ABSTRACTThere are few available approaches for time delay estimation (TDE) which offer with a potential to be implemented as real-time-embedded systems. Most of these lose accuracy in reverberant and highly noisy environments. In this paper, we initially proposed a 2-dimensional (2D) signal based on the cross-correlation. Then, we proposed two approaches for TDE, based on this 2D signal. There are two main advantages for using the proposed approaches. The first merit is that using a large amount of information about the correlation between the received signals allows a more robust TDE against noise and reverberation, when compared to the other possible techniques. The second one is that the number of samples to calculate the TDE algorithms is decreasing significantly, which is reducing the computational complexity. This paper also proposes a feasible hardware structure to implement the proposed approaches on digital signal processor embedded systems in real time.

Robust time delay estimation based direction finding in lp-space

ABSTRACTA robust time delay estimation (TDE)-based direction-finding method for multipath signals in the presence of impulsive noise is proposed in this paper. Compared to traditional algorithms, developed for Gaussian distribution, this method performs more robust in the presence of non-Gaussian noise. The robust TDE is first obtained by utilising the matched filtering in -space which is less sensitive to outliers. Then direction-of-arrival (DOA) is estimated by ‘steering vector fitting’ approach based on the delay estimates. An iterative approach is employed to estimate each multipath component separately and improve the estimation accuracy. The method is developed in -space, which minimises the -norm of residual fitting error vector instead of the Frobenius norm. The number of antennas required can be less than that of the sources. Simulation results show that the method outperforms several outliers-resistant algorithms both in cases of impulsive noise and Gaussian distribution noise.

Robust Acoustical Gas Leak Detection in the Presence of Correlated, Uncorrelated and Impulsive Noises, Using Wavelet-Based Optimized Residual Complexity

Leak detection based on acoustic emission needs robust time delay estimation in the presence of various types of noise. Considering several aspects of leakage signal and noise, the present work introduces wavelet based optimized residual complexity (WORC), in order to minimize the probability of false alarms in the presence of correlated, uncorrelated, and impulsive noise. The proposed method utilizes two approaches simultaneously. First, it optimizes residual complexity “for robustness against both correlated and uncorrelated noise”, then it employs wavelet basis in order to reduce the complexity of mixture of correlated and impulsive noise. In order to compare the resolution and percentage of false alarms of WORC with those of optimized residual complexity and cross correlation, we used both experimental and simulated leakage signals of a gas pipe. The results demonstrate the superiority of WORC in term of robustness in the presence of correlated, uncorrelated, and impulsive noise.

On robust time delay estimation in room acoustic environments

Time delay estimation, which serves as the first stage that feeds into subsequent processors for localizing and tracking radiating sources, has been an active research topic for decades. This paper deals with time delay estimation in room acoustic environments. The major focus is on developing algorithms that can achieve robust time delay estimates in reverberant and noisy environments. Three approaches are investigated, i.e., the blind channel identification based method, the multichannel cross correlation based technique, and a method based on the use of the Householder transformation. The first approach achieves its robustness with respect to reverberation by taking into account reverberation in the problem formulation; the second one improves the robustness by exploiting the redundancy provided by multiple microphones, and the third method attempts to separate noise and reverberation from the signal components. Experimental results are presented to illustrate their performance and pros and cons in room acoustic environments where reverberation and noise are commonly encountered.

Robust strong lensing time delay estimation

Strong gravitational lensing of time variable sources such as quasars and supernovae creates observable time delays between the multiple images. Time delays can provide a powerful cosmographic probe through the ``time-delay distance'' involving the ratio of lens, source, and lens-source distances. However, light curves of lensed images have measurement gaps, noise, systematics such as microlensing from substructure along an image line of sight, and no a priori functional model, making robust time-delay estimation challenging. Using Gaussian process techniques, we demonstrate success in accurate blind reconstruction of time delays and reduction in uncertainties for real data.

Adaptive Time Delay Estimation Based on Robust Cyclic Correlation Estimator

Based on the proposed efficient cyclic correlation estimator by exploiting normalized infinite norm, this paper develops a robust adaptive time delay estimation method which provides additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Simulations demonstrate the reliable and robust performance of the proposed cyclic correlation estimator and its corresponding time delay estimation algorithm.

A new correntropy based TDE method under α-stable distribution noise environment

This paper presents a robust time delay estimation algorithm for the α-stable noise based on correntropy. Many time delay estimation algorithms derived for impulsive stable noise are based on the theory of Fractional Lower Order Statistics (FLOS). Unlike previously introduced FLOS-type algorithms, the new algorithm is proposed to estimate the time delay by maximizing the generalized correlation function of two observed signals needing neither prior information nor estimation of the numerical value of the stable noise’s characteristic exponent. An interval for kernel selection is found for a wide range of characteristic exponent values of α-stable distribution. Simulations show the proposed algorithm offers superior performance over the existing covariation time delay estimation, least mean p-norm time delay estimation and achieves slightly improved performance than fractional lower order covariance time delay estimation at lower signal to noise ratio when the noise is highly impulsive

Robust Time Delay Estimation Using Variable Step-Size Affine Projection Algorithm with M-estimators

摘要: 该文提出了一种基于M估计变步长自适应仿射投影方法的稳健时延估计(TDE)算法。该算法将自适应仿射投影算法应用于时延估计,无须事先假定信号和噪声的统计特性,自适应调整自身参数;应用稳健M估计理论,抵消重尾噪声干扰。数值仿真表明,在高斯噪声、非高斯噪声甚至冲激噪声的干扰下,该文算法比高阶统计量法和最小均方自适应法有更强的稳健性和更高的估计精度。 关键词: 时延估计; M估计; 变步长自适应仿射投影算法; 高阶统计量

A New Subspace-Based Time Delay Estimation of Chirp Spread Spectrum

For robust time delay estimation, we propose a new subspace-based algorithm which estimates the phase shift not between the adjacent samples of the dechirped sinusoid but between the dechirped sinusoids in different bands. We analyze the proposed and the conventional method in AWGN channel and verifiy the proposed algorithm through Monte-carlo simulations in AWGN and multi-path channels.

Robust propagation velocity estimation of gastric electrical activity by least mean p-norm blind channel identification

The propagation of the gastric slow wave is one of the most important spatial characteristics of gastric electrical activity (GEA). The time delay estimation (TDE) is an effective approach to quantitatively assessing the propagation velocity of GEA. Traditional TDE analyses are developed under the condition reported that the background noise in GEA analysis is Gaussian distributed. Due to the effects of spikes and/or motion artifacts, the GEA obtained from gastric serosal electrodes often contains sharp transitions. This paper proposes robust time delay estimation based on least mean p-norm blind channel identification (BCILMP) under alpha-stable noise condition. Compared with the least mean square time delay estimation (LMSTDE), the BCILMP provides better performance in the impulsive noise environments. The robustness of the proposed method is demonstrated through computer simulations in both Gaussian and alpha-stable noise environments. The results of the propagation velocity of real data obtained from gastric serosal electrodes in gastroparetic patients show that the propagation velocity in gastroparetic patients is slower than in the normal subjects reported in the literature, and the slow-wave propagation is directed proximally to distally from the corpus toward the pylorus but not all the variability of the propagation velocity increases monotonously.

Robust Adaptive Time Delay Estimation for Speaker Localization in Noisy and Reverberant Acoustic Environments

Two adaptive algorithms are presented for robust time delay estimation (TDE) in acoustic environments with a large amount of background noise and reverberation. Recently, an adaptive eigenvalue decomposition (EVD) algorithm has been developed for TDE in highly reverberant acoustic environments. In this paper, we extend the adaptive EVD algorithm to noisy and reverberant acoustic environments, by deriving an adaptive stochastic gradient algorithm for the generalized eigenvalue decomposition (GEVD) or by prewhitening the noisy microphone signals. We have performed simulations using a localized and a diffuse noise source for several SNRs, showing that the time delays can be estimated more accurately using the adaptive GEVD algorithm than using the adaptive EVD algorithm. In addition, we have analyzed the sensitivity of the adaptive GEVD algorithm with respect to the accuracy of the noise correlation matrix estimate, showing that its performance may be quite sensitive, especially for low SNR scenarios.

Alpha-stable modeling of noise and robust time-delay estimation in the presence of impulsive noise

A new representation of audio noise signals is proposed, based on symmetric /spl alpha/-stable (S/spl alpha/S) distributions in order to better model the outliers that exist in real signals. This representation addresses a shortcoming of the Gaussian model, namely, the fact that it is not well suited for describing signals with impulsive behavior. The /spl alpha/-stable and Gaussian methods are used to model measured noise signals. It is demonstrated that the /spl alpha/-stable distribution, which has heavier tails than the Gaussian distribution, gives a much better approximation to real-world audio signals. The significance of these results is shown by considering the time delay estimation (TDE) problem for source localization in teleimmersion applications. In order to achieve robust sound source localization, a novel time delay estimation approach is proposed. It is based on fractional lower order statistics (FLOS), which mitigate the effects of heavy-tailed noise. An improvement in TDE performance is demonstrated using FLOS that is up to a factor of four better than what can be achieved with second-order statistics.