Performance Of Time Delay Estimation Research Articles

Super-resolution time delay estimation using exponential kernel correlation in impulsive noise and multipath environments

The time delay estimation (TDE) is an important and fundamental part in wireless communication signal processing, which has been widely used in localization, intelligent navigation, and radio monitoring. However, it remains a challenging task to support such TDE mechanisms with super-resolution in impulsive noise and multipath environments. Aiming at improving the TDE performance in both impulsive noise and multipath environments, in this paper, a novel super-resolution TDE method is proposed by using the defined exponential kernel correlation (EKC) function. In the proposed TDE method, EKC function is defined to suppress the non-Gaussian impulsive noise. Specifically, the theoretical analysis is also given to prove its boundedness and its effectiveness in non-Gaussian impulsive noise suppression. Further, a novel super-resolution TDE method is proposed by combining EKC function and multiple signal classification (MUSIC) method, which can significantly improve the estimation performance in terms of resolution and accuracy with both non-Gaussian impulsive noise and multipath environments. Finally, simulation experiments demonstrate that the proposed super-resolution TDE method maintains good performance even with a low characteristic exponent and low generalized signal-to-noise ratio (GSNR).

Weighting function modification used for phase transform-based time delay estimation

Generalized cross-correlation is considered as the most straightforward time delay estimation algorithm. Depending on various weighting function, different methods were derived and a straightforward method, named phase transform (PHAT) has been widely used. PHAT is well-known for its robustness to reverberation and its sensitivity to noise, which is partly due to the fact that PHAT distributes same weights to the frequencies dominated by signal or noise. To alleviate this problem, two weighting functions are proposed in this paper. By taking a posteriori signal-to-noise ratio (SNR) into account to classify reliable and unreliable frequencies, different weights could be assigned. The first proposed weighting function borrows the idea of binary mask and distributes same weights to frequencies in same set, whereas, the second one assigns weights based on coherence function. Experiments showed the robustness of proposed methods to reverberation and noise for improving the performance of time delay estimation through various criteria.

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.

고 해상도 버퍼 설계를 통한 전달 정렬의 측정치 시간 지연 보상 기법

In this paper, we propose a time delay compensation method for transfer alignment using a high-resolution buffer design. Time delay degrades the performance of transfer alignment, resulting in subsequent mission failure. The value of such a time delay may not be constant and varies indefinitely depending on the system configuration or operating environment, which reduces the time delay estimation performance using general Kalman filtering. Using a state buffer is one of the methods to solve this problem. This paper presents a new time delay estimation method using a time delay candidate buffer. The proposed scheme can finely set the interval of time delay candidates and does not require a separate memory. In addition, a method for designing an appropriate time delay candidate buffer interval according to the activation of a vehicle and the performance of the MINS is presented. Simulations verified that the proposed technique could reduce the memory requirement and improve the transfer alignment performance for finely changing the time delay.

Reflection Seismic Interferometry via Higher-Order Cumulants to Solve Normal Moveout Stretch

Normal moveout (NMO) correction is an important step in seismic data processing. Conventional NMO methods, however, suffer from serious stretching distortions, especially at the shallow layer and far-offset traces. These distortions are not conducive to subsequent data processing at all. Reflection seismic interferometry, the cores of which are cross-correlation and stacking, is the latest technology to achieve noise suppression and signal enhancement. Compared with cross-correlation, higher-order cumulants (HOCs) have a better performance of time delay estimation and signal enhancement. To fully exploit its advantages, we propose reflection seismic interferometry based on HOC instead of cross-correlation to solve NMO stretching in this letter. Our proposed method is first tested on synthetic examples and then applied to field data from eastern China. The corresponding results demonstrate that the proposed data-driven method, without the need for velocity information, not only solves the far-offset traces’ stretching distortions of NMO correction, but also has a better noise suppression effect compared with the other two existing methods. These advancements are important to improve the resolution of seismic data and increase amplitude fidelity.

Radio signal time delay estimation performance of TDOA/SSR-based source localization systems

The work is devoted to the development of an algorithm for the optimal Radio Signal Time Delay Estimation Performance in passive location systems of stationary targets based on the TDOA method in two-dimensional space. A realistic model of the radio signal at the input of sensors (base station receivers) is considered, considering the random power value as a function of the distance to the source. The optimal estimate is based on the strategy of maximum posterior probability density. The calculation of the statistical characteristics of the obtained estimate of the radio signal delay time is carried out. The Bayesian Cramér - Rao lower bound (BCRLB) of the latency estimate is calculated. It is shown that the use of a priori statistical information on the path loss of a radio signal model can improve the accuracy of estimating the propagation delay time of a radio signal in TDOA/SSR-Based Source Localization Systems.

Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals.

This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the Cramér–Rao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques.

Positioning in a Multipath Channel Using OFDM Signals With Carrier Phase Tracking

In developing a high accuracy terrestrial radio navigation system, as a complement to a global navigation satellite system (GNSS), it is recognized that the performance of time delay estimation is proportional to, and thereby limited by, the signal bandwidth. Given a possibly narrow signal bandwidth, the central carrier phase can, alternatively, provide a better distance accuracy, though the central carrier phase cycle ambiguity should be resolved. In practice, the carrier phase may be perturbed by multipath. In this paper, considering an orthogonal frequency division multiplexing (OFDM) signal, we propose a two-step carrier phase estimation method to reduce the error introduced by multipath. First, the propagation delay of the LoS path is coarsely determined, then the carrier phase is estimated using the earlier determined coarse time delays. Furthermore, a positioning model only based on carrier phase estimates is presented in this paper. The proposed technique is evaluated by statistical analyses and a simulated OFDM-based terrestrial positioning system in different roadway multipath environments. The results show that the impact of multipath on carrier phase estimation can be largely mitigated, so that the carrier phase can be used for precise positioning. In addition, fixing the integer carrier phase cycle ambiguities can significantly reduce the time for the position solution to converge to high precision.

A class of multichannel sparse linear prediction algorithms for time delay estimation of speech sources

Time delay estimation (TDE), which is also called time difference of arrival estimation, is an important yet challenging problem in room acoustic environments where reverberation and noise coexist. The multichannel cross-correlation coefficient algorithm extends the traditional cross-correlation method from the two- to the multiple-channel cases and exploits the spatial information among all microphones to improve the robustness of TDE with respect to noise. The multichannel spatiotemporal prediction algorithm generalizes the multichannel cross-correlation coefficient algorithm by incorporating both the spatial and temporal information to make TDE robust to reverberation. This multichannel spatiotemporal prediction algorithm, however, is sensitive to noise. In this work, we attempt to improve the robustness of this algorithm by making it robust to both reverberation and noise. Based on the sparsity of the prediction coefficient matrix of speech signals, a class of multichannel sparse linear prediction algorithms, including the multichannel spatiotemporal sparse prediction and the multichannel spatiotemporal group sparse prediction, are developed for TDE. The multichannel cross-correlation coefficient and multichannel spatiotemporal prediction algorithms are unified from a TDE performance perspective via an F/ℓ1-norm (or F/ℓ1,2-norm) optimization model, which is solved by an alternating direction method of multipliers. The two new algorithms also respectively construct a set of time delay estimators, which make different tradeoffs between prewhitening and non-prewhitening by adjusting a regularization parameter.

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.

Compressed Sensing OFDM Time Delay Estimation Based on Atomic Evolution and Elimination

Aiming at the problem of time delay estimation for orthogonal frequency division multiplexing (OFDM) signals with small samples, a new compressed sensing time delay estimation method based on atomic evolution and elimination is proposed. This method introduces the idea of evolution and elimination from Darwin's evolutionism, abandons the fixed and invariable way of grid points in the traditional algorithm, takes grid points as adjustable parameters, and updates grid points in each iteration process to promote the evolution of atoms. In order to deal with the off-grid effect, the exponential function is directly modified instead of linear approximation, which effectively eliminates the modeling error caused by the original off-grid algorithm. In the process of calculation, the atomic elimination mechanism is adopted, which reduces the amount of calculation and improves the speed of it. The simulation results show that the proposed algorithm can significantly improve the performance of time delay estimation, especially when using coarse grids. Based on the universal software radio peripheral (USRP), the validity of the proposed algorithm is verified by the actual signal.

Time delay estimation based on log-sum and lp-norm penalized minor component analysis

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320-327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation.

Characteristics Analysis and Modeling of Fault Sound and Background Noise of Large Central Air Conditioner

Performance of time delay estimation (TDE) algorithm directly affects effect of electromechanical equipment fault sound source positioning based on TDE. Designing more accurate algorithm based proper models of background noise and fault sound can improve accuracy of localization. Firstly, this paper analyzes the characteristics of noise and fault sound of external central air conditioners, then, models environmental noise using Alpha distribution. Finally, Analyzes measured data and preprocess them, realize the more appropriate modeling of environmental noise and eliminate operating noise sources interference. This study has a guiding significance in large-scale mechanical and electrical equipment failure sound source signal modeling, and is helpful to research on TDE algorithm.

Fast time-delay measurement for integrated pulsar pulse profiles

PurposeThe authors proposed a new method of fast time delay measurement for integrated pulsar pulse profiles in X-ray pulsar-based navigation (XNAV). As a basic observation of exact orientation in XNAV, time of arrival (TOA) can be obtained by time delay measurement of integrated pulsar pulse profiles. Therefore, the main purpose of the paper is to establish a method with fast time delay measurement on the condition of limited spacecraft’s computing resources.Design/methodology/approachGiven that the third-order cumulants can suppress the Gaussian noise and reduce calculation to achieve precise and fast positioning in XNAV, the proposed method sets the third-order auto-cumulants of standard pulse profile, the third-order cross-cumulants of the standard and the observed pulse profile as basic variables and uses the cross-correlation function of these two variables to estimate the time delay of integrated pulsar pulse profiles.FindingsThe proposed method is simple, fast and has high accuracy in time delay measurement for integrated pulsar pulse profiles. The result shows that compared to the bispectrum algorithm, the method improves the precision of the time delay measurement and reduced the computation time significantly as well.Practical implicationsTo improve the performance of time delay estimation in XNAV systems, the authors proposed a novel method for XNAV to achieve precise and fast positioning.Originality/valueCompared to the bispectrum algorithm, the proposed method can improve the speed and precision of the TOA’s calculation effectively by using the cross-correlation function of integrated pulsar pulse profile’s third-order cumulants instead of Fourier transform in bispectrum algorithm.

High resolution time-delay estimation of underwater target geometric scattering

The geometric scatterings carry the information of the shape of an underwater target. While the time-delay of the weak geometric scattering exists in the received signal cannot be obtained accurately by the conventional time-delay estimation methods because of the limit of the main-lobe width and the interferences from the side-lobe. In this paper, we propose a high resolution time-delay estimation (HRTDE) scheme consisting of two steps. Firstly, when a linear-frequency-modulated (LFM) pulse is transmitted by sonar, the dechirping method transforms the geometric scatterings with different time-delays into multiple single-frequency components respectively, in which the frequency of the dechirped signal shows a linear relationship with the time-delay of the geometric scattering. Then the multiple signal classification (MUSIC) algorithm is adopted to increase the spectrum resolution when multiple single-frequency signals exist in the dechirped signal and the frequency interval is smaller than the frequency resolution limit of the Fourier transform. Simulation results show that the main lobe of the proposed scheme is sharper and with less interference from the side-lobe, compared with the conventional time-delay estimation methods. The results from the anechoic pool experiment demonstrate that the proposed scheme achieves a better time-delay estimation performance for the weak geometric scattering generated by the bottom edge of the underwater target model than match filter based methods.

Cross-correlation analysis and time delay estimation of a homologous micro-seismic signal based on the Hilbert–Huang transform

A micro-seismic signal's transient features are non-stationary. The traditional weighted generalized cross-correlation (GCC) algorithm is based on the cross-power spectrum density. This algorithm diminishes the performance of the time delay estimation for homologous micro-seismic signals. This paper analyzed the influence of calculation error on the cross-power spectrum density of a non-stationary signal and proposed a new cross-correlation analysis and time delay estimation method for homologous micro-seismic signals based on the Hilbert–Huang transform (HHT). First, the original signals are decomposed into intrinsic mode function (IMF) components using empirical mode decomposition (EMD) for de-noising. Subsequently, the IMF components and the original signals are analyzed using a cross-correlation analysis. The IMF components are subsequently remodeled at different scales using the Hilbert transform. The marginal spectrum density is obtained via a time integration of the remodeled components. The cross-marginal spectrum density of the two signals can also be obtained. Finally, the cross-marginal spectrum density is used in the weighted GCC algorithm for time delay estimation instead of the cross-power spectrum density. The time delay estimation is determined by searching for the weighted GCC function peak. The experiments demonstrated the superior time delay estimation performance of the new method for non-stationary transient signals. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper.

클러스터링 기법을 이용한 음원의 위치추정 성능향상

Source localization has developed in various fields of signal processing including radar, sonar, and wireless communication, etc. Source localization can be found by estimating the time difference of arrival between the each of sensors. Several methods like the NLS(Nonlinear Least Square) cost function have been proposed in order to improve the performance of time delay estimation. In this paper, we propose a clustering method using the four sensors with the same aperture as previous methods of using the three sensors. Clustering method can be improved the source localization performance by grouping similar estimated values. The performance of source localization using clustering method is evaluated by Monte Carlo simulation.

Coded excitation in space and time for the identification of geometric parameters using audio transducer arrays

Impulse responses of a room can provide a lot of geometric information of acoustic systems. Loudspeaker and microphone array positions, locations of walls and scattering bodies are representative examples. To extract geometric information from the room impulse responses, the measurement time should be short enough such that the degradation of time delay estimation performance due to non-stationary environmental noises can be minimized. In this work, we attempt to enhance the SNR of the short-term measurement, by using a coded excitation technique in space and time. Both the spatially and temporally coded excitation signals are applied to a set of loudspeakers combined with microphones, and it is shown that the measurement time can be shortened with minimal loss of SNR and time delay estimation performance.

The Performance Correlation Hilbert Time-Delay Estimation for Passive Detection

Passive time difference detection method is distance, high speed and good concealment which has broad military application prospects. One of the key technologies for passive detection is to extract the time lag through effective signal processing. Relevant method is the most basic method to estimate the time difference and is the basic theory of all correlative time-delay estimation algorithms. The method is simple. But good results rely on the spectrum characteristics of signal and noise is ideal. Time delay estimation based on Hilbert transform is the expansion of the generalized correlation time-delay estimation method which changes the correlation function from accidentally symmetry into odd symmetry. Detecting correlation peak is converted into zero crossing detection. The method sharps the main peak value point and improved the precision of time delay estimation which gets better time-delay estimation performance in the narrowband signal.

Joint maximum likelihood time-delay estimation for LTE positioning in multipath channels

This paper presents a joint time-delay and channel estimator to assess the achievable positioning performance of the Long Term Evolution (LTE) system in multipath channels. LTE is a promising technology for localization in urban and indoor scenarios, but its performance is degraded due to the effect of multipath. In those challenging environments, LTE pilot signals are of special interest because they can be used to estimate the multipath channel and counteract its effect. For this purpose, a channel estimation model based on equi-spaced taps is combined with the time-delay estimation, leading to a low-complexity estimator. This model is enhanced with a novel channel parameterization able to characterize close-in multipath, by introducing an arbitrary tap with variable position between the first two equi-spaced taps. This new hybrid approach is adopted in the joint maximum likelihood (JML) time-delay estimator to improve the ranging performance in the presence of short-delay multipath. The JML estimator is then compared with the conventional correlation-based estimator in usual LTE conditions. These conditions are characterized by the extended typical urban (ETU) multipath channel model, additive white Gaussian noise (AWGN) and LTE signal bandwidths equal to 1.4, 5 and 10 MHz. The resulting time-delay estimation performance is assessed by computing the cumulative density function (CDF) of the errors in the absence of noise and the root-mean-square error (RMSE) and bias for signal-to-noise ratio (SNR) values between −20 and 30 dB.