Time Delay Estimation Algorithm Research Articles

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

Echo Delay Estimation Using Algorithms Based on Cross-correlation

The following paper introduces a study on correlation-based time delay estimation algorithms. Its main purpose is to suggest a number of algorithms, which are the most suitable for real-time processing of speech signals. Time delay estimation is an important topic during transmission of voice signals over packet-switching telecommunication systems. An echo delay problem associated with IPbased transport networks is discussed in the text. An experimental comparison of the performance of several methods based on cross-correlation, normalized cross-correlation and generalized crosscorrelation to estimate the true time delay between two speech signals is presented, too.

A Fast and Efficient Time-Delay Estimation Algorithm for Sound Localization

Time-delay estimation is an important research topic of sound localization. According to the specificity of time-delay in sound localization, a fast and efficient time-delay estimation algorithm is proposed based on the principle of fast linear cross correlation. Relative to the original time-delay estimation algorithm based on fast linear cross correlation, the proposed algorithm has enormous advantages in the time complexity and space complexity. The computing speed is twice faster than the original time-delay estimation algorithm, while the size of the memory space used is half of the original time-delay estimation algorithm.

Time Delay Estimation Algoritms for Echo Cancellation

The following case study describes how to eliminate echo in a VoIP network using delay estimation algorithms. It is known that echo with long transmission delays becomes more noticeable to users. Thus, time delay estimation, as a part of echo cancellation, is an important topic during transmission of voice signals over packetswitching telecommunication systems. An echo delay problem associated with IP-based transport networks is discussed in the following text. The paper introduces the comparative study of time delay estimation algorithm, used for estimation of the true time delay between two speech signals. Experimental results of MATLab simulations that describe the performance of several methods based on cross-correlation, normalized crosscorrelation and generalized cross-correlation are also presented in the paper.

Acoustic Source Localization Based on Generalized Cross-correlation Time-delay Estimation

In the existing acoustic emission (AE) localization methods, the estimation localization method based on the time difference of arrival (TDOA) has less computation and higher accuracy of positioning and is easy to implement the real-time systems and thus it becomes a commonly used method in the acoustic source localization methods. In this method, the most important thing is time delay estimation (TDE) that directly affects the accuracy of positioning. In this paper, we analyze the performance of several weighting functions in the generalized cross-correlation time-delay estimation algorithm and give the simulation results. By comparison, PHAT weighting is characterized by small fluctuations, sharp peak and strong anti-jamming ability and it is the best choice for acoustic source localization in the generalized cross-correlation time-delay estimation algorithm.

MP-based Algorithm in Node Localization of Wireless Sensor Network

W ireless sensor network (WSN) has attracted more and more research interest b ecause of its various applications in distributed monitoring, communication, and computing . For most applications, developing a distributed practical positioning algorithm is probably the most important and challenging task. As a high-resolution method of parameter estimation , m atrix pencil (MP) algorithm can be used in node localization. W e put forward a novel range-based node localization method for WSN i n th is paper . The new scheme employ s MP algorithm for multi-path time-delay estimation , multilateral localization for absolute positioning and Taylor algorithm or Chan algorithm to solve non-linear equations . T he proposed method has some key superiority over traditional loca lization methods, such as higher positioning accuracy, smaller computational amount, suppression over non-Gaussian noise, energy saving, faster executing, etc. Simulations show that the accuracy of MP algorithm for time of arrival (TOA) estimation is low (ns-level) and non-Gaussian noise can be well inhibited. T he impact of some factors such as signal to noise ratio (SNR) and the number of nodes are also studied in this paper.

A novel time delay estimation using chirp signals robust to sampling frequency offset for a ranging system

We propose a novel time delay estimation algorithm using a training and data sequence composed of chirp signals for a ranging system with frequency offset. This training and data sequence can be used for SNR enhancement using the proposed denoising method. Then, a subspace-based algorithm, ESPRIT, is applied to estimate the time delay of the first arrival path. We analyze the estimation performance of ESPRIT based on the proposed denoising method and verify it through Monte-carlo simulations in AWGN and a multi-path channel.

A comparison of sound localization methods in a room environment using a spherical microphone array.

Enclosures are one of the most challenging environments for source localization, especially for teleconferencing where the main goal is to enhance desired speech signals while suppressing noise. This can be accomplished using beamforming but the speech source locations must be known. Three passive methods will be compared for room environment localization: generalized cross-correlation (GCC), spherical harmonic-based multiple signal classification (MUSIC), and spatial power signal characteristics and gradients (SPSGs). SPSG is a new localization algorithm designed for room environments based on wideband third order beamforming and power signal characteristics, differences, and gradients. MUSIC is a subspace method that relies on an eigen-decomposition of the beam correlation matrix and GCC is a time delay estimation algorithm based on microphone signal correlation. MUSIC and GCC are methods that perform well in their optimal environments but suffer in noisy and reverberant conditions. Results will be shown for multiple source situations with reverberation and noisy interferers.

The Algorithm of Acoustic Location for Boiler Tube Leakage

The time delay estimation algorithm based on generalized cross correlation, can suppress the noise power effectively. More accurate result can be gotten by this method of time delay estimation. It is proved by simulation and experimentation that the estimated value of time delay given by generalized cross correlation method is more accurate than which is given by basic correlation method when the signal and noise ratio is stationary, thus the location result based on the time delay is more accurate.

Positioning of Acoustic Emission Source by Using Sub-gradient Projection Based on Energy Attenuation Model

Because of containing several model waveforms, the source rub-impact acoustic emission(AE) signal transmitting in complicated body will cause waveform distortion for varying transmission speed of each model and dispersion effect. It is difficult to achieve exact source location by traditional time difference location. An energy attenuation model is established on the basis of the characteristic of approximately inverse ratio between propagation energy and source distance, the location of the AE source is limited in a hypersphere in which the circle centre and radius are all the energy ratio functions of a sensors pair, and uses adaptive sub-gradient projection to approximate to the source location. As the method is based on energy ratio instead of energy, the location accuracy will not be significantly affected even if the energy of AE source has a significant change. When multiple sensors pairs are installed more hyperspheres can be decided, so this method is also applicable to positioning array composed of multiple sensors. The experiment results show that this algorithms has higher location accuracy than Time Delay Estimation algorithm, it also has good convergence and lower computation complexity.

Modified ESPRIT (M-ESPRIT) algorithm for time delay estimation in both any noise and any radar pulse context by a GPR radar

This paper presents M-ESPRIT, a modified version of the ESPRIT algorithm, for the purpose of time delay estimation of backscattered radar signals. The proposed algorithm takes both the transmitted pulse shape and any noise into account. It can process raw data from experimental device without the preprocessing which would be required with the conventional ESPRIT algorithm.

New HB-weighted time delay estimation algorithm under impulsive noise environment

The traditional HB-weighted time-delay estimation (TDE) method degenerates under the impulsive noise environment. Two new time-delay estimation methods are proposed based on fractional lower order statistics (FLOS) according to the impulsive characteristics of fractional lower order α-stable noises. Theoretic analysis and computer simulations indicate that the proposed covariation based HB weighted (COV-HB) algorithm can suppress impulsive noises in one received signal for 1 ≤ α ≤ 2, whereas the other proposed fractional lower order covariance-based HB weighted (FLOC-HB) algorithm has robust performance under arbitrary impulsive noise conditions for the whole range of 0 <α ≤ 2.

Fast Iterative Maximum-Likelihood Algorithm (FIMLA) for Multipath Mitigation in the Next Generation of GNSS Receivers

In this paper, we efficiently solve the maximum likelihood (ML) time-delay estimation problem for GNSS signals in a multipath environment. Exploiting the GNSS signal model structure and the spreading code periodicity, we develop an efficient implementation of the Newton iterative likelihood maximization method by finding simple analytical expressions for the first and second derivatives of the likelihood function. The proposed fast iterative ML algorithm (FIMLA) for timedelay estimation, which uses the correlation function of the received signal with its local replica, is shown to be an attractive technique for mitigation of closely-spaced multipath arrivals. For the future modernized GPS and the European Galileo signals based on binary offset carrier (BOC) waveforms, the correlation function has multiple positive and negative peaks leading to potential tracking ambiguities. Instead of the standard crosscorrelation, we propose an implementation characterized by a different choice of the local replica so as to cancel the sub-carrier phase, thus eliminating ambiguities. The asymptotic performance of FIMLA is analyzed by deriving the corresponding Cramer-Rao bound (CRB). Representative simulation examples are included to illustrate the FIMLA is performance for delay estimations in the presence of multipath for both C/A code and BOC signals.

Time Delay Estimation via Minimum Entropy

Time delay estimation (TDE) is a basic technique for numerous applications where there is a need to localize and track a radiating source. The most important TDE algorithms for two sensors are based on the generalized cross-correlation (GCC) method. These algorithms perform reasonably well when reverberation or noise is not too high. In an earlier study by the authors, a more sophisticated approach was proposed. It employs more sensors and takes advantage of their delay redundancy to improve the precision of the time difference of arrival (TDOA) estimate between the first two sensors. The approach is based on the multichannel cross-correlation coefficient (MCCC) and was found more robust to noise and reverberation. In this letter, we show that this approach can also be developed on a basis of joint entropy. For Gaussian signals, we show that, in the search of the TDOA estimate, maximizing MCCC is equivalent to minimizing joint entropy. However, with the generalization of the idea to non-Gaussian signals (e.g., speech), the joint entropy-based new TDE algorithm manifests a potential to outperform the MCCC-based method

The robustness analysis of DLMP algorithm under the fractional lower-order α-stable distribution environments

The direct least mean p-norm (DLMP) algorithm is a robust algorithm for time delay estimation under both Gaussian and fractional lower-order α-stable noise conditions. This paper briefly reviews the fundamental theory of α-stable distribution, abstracts two important theorems of fractional lower-order statistics, and under the Gaussian signal assumption, analyzes the convergence property of the DLMP algorithm, including the convergence property of the cost function, the unbiasness of the estimator and the appropriate selection of the convergence parameter μ. The improved performance of DLMP algorithm is clearly demonstrated through comprehensive simulations and detailed data analysis for estimating the latency changes of evoked potential (EP) signals. In the case of p=2, it is presented that DLMP algorithm keeps consistent with DLMS algorithm, which is based on second-order statistics, involving the cost function, the unbiasness of the estimator and the selection of the convergence parameter. Therefore, this paper proves that DLMP algorithm is a generalization of DLMS algorithm in α-stable distribution noisy environments.

Research on Single Base-Station Distance Estimation Algorithm in Quasi-GPS Ultrasonic Location System

In order to identify each base-station in quasi-GPS ultrasonic location system, a unique pseudo-random code is assigned to each base-station. This article primarily studies the distance estimation problem between Autonomous Guide Vehicle (AGV) and single base-station, and then the ultrasonic spread-spectrum distance measurement Time Delay Estimation (TDE) model is established. Based on the above model, the envelope correlation fast TDE algorithm based on FFT is presented and analyzed. It shows by experiments that when the m sequence used in the received signal is as same as the reference signal, there will be a sharp correlation value in their envelope correlation function after they are processed by the above algorithm; otherwise, the will be no prominent correlation value. So, the AGV can identify each base-station easily.

Transform-Domain Adaptive Constrained Normalized-LMS Filtering Scheme for Time Delay Estimation

The convergence speed of the conventional adaptive LMS algorithms for time delay estimation (TDE) is highly dependent on the spectral distribution of the desired random source signals of interest, thus the performance of TDE might be degraded, dramatically. To solve this problem, in this letter, a DCT-transform domain constrained adaptive normalized-LMS filtering scheme, referred to as the adaptive constrained DCT-LMS algorithm, is devised for TDE. Computer simulation results verify that the proposed scheme can be used to achieve desired performance, for input random signals with different spectral distributions; it outperforms the unconstrained DCT-LMS and time-domain constrained adaptive LMS algorithms.

Least Squares Based Timing Acquisition and Tracking for Closely Spaced Multipath in DS-CDMA

In this paper, we present a time-delay estimation algorithm in a closely spaced multipath environment for a direct-sequence code division multiple access (DS-CDMA) systems. The proposed scheme first converts the observed signal to the frequency domain by Fast Fourier Transform (FPT). We then formulate a nonlinear estimation problem, and convert it into a large scale linear least squares problem. We apply conjugate gradients iterations on the resulting normal equations to obtain the solution. Unlike the methods which invoke the criterion of nonlinear least squares, the proposed scheme can achieve higher resolution. The delay estimation is combined with a tracking algorithm based on a FIR prefilter. Simulation results confirm the effectiveness of the proposed algorithm in both AWGN and Rayleigh fading channels.

Explicitly-Adaptive Time Delay Estimation for Wide-Band Signals

A new method of explicitly adaptive time delay estimation (EATDE) algorithm is proposed for estimating a varying time delay parameter. The proposed method is based on the Haar wavelet transform of cross-correlations. The proposed algorithm can be viewed as a gradient-based optimization of lowpass filtered cross-correlations, but requires less computational power. The algorithm shows a global convergence property for wide-band signals with uncorrelated noises. A convergence analysis including mean behavior, mean-square-error behavior, and steady-state error of delay estimate is given. Simulation results are also provided to demonstrate the performance of the proposed algorithm.

Estimation of direction of arrival using information theory

Estimating the direction of arrival (DOA) of an acoustic source relies on the successful estimation of the relative delay between pairs of microphone signals. Processing is performed at the current time by operating on blocks of recorded data. When these recordings are performed in environments of strong multipath reflections, algorithms often fail to distinguish between the true DOA and that of a dominant reflection. In this letter, we assume Gaussianity of the source signal and use an information-theoretical measure, often met in blind source separation algorithms, to derive a robust DOA estimator, even under significant reverberant conditions. We discuss the most popular algorithm for time delay estimation, namely, the generalized cross-correlation method, and demonstrate under certain conditions its connection to the proposed one. Performance is demonstrated for both algorithms with sets of simulated results as a function of different reverberation times, microphone spacing, and data block size. The results indicate that the examined framework can accurately track the DOA of a typical acoustic source.