Generalized Cross Correlation Research Articles

Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.

Spatial Localization of a Transformer Robot Based on Ultrasonic Signal Wavelet Decomposition and PHAT-β-γ Generalized Cross Correlation.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

Modified Frequency-Sliding Generalized Cross Correlation for Time Delay Difference Estimation of Microphone Array

Modified Frequency-Sliding Generalized Cross Correlation for Time Delay Difference Estimation of Microphone Array

Research on Time Delay Estimation Method of Partial Discharges Signal with Improved Weighted Function

Positioning systems based on the Time Difference of Arrival (TDOA) often struggle to accurately estimate time delays for partial discharge signals in complex electromagnetic environments. To address this, we introduce an improved joint-weighted generalized cross-correlation (GCC) time delay estimation algorithm. Traditional GCC time delay estimations, when reliant on conventional weighted functions, significantly underperform in situations characterized by low signal–noise ratios (SNRs). Our proposed improved joint weighted function combines the advantages of both phase transform (PHAT) and correlation transform (SCOT) weightings, resulting in a composite function. Compared to the conventional weighted GCC, the improved joint weighted GCC displays a more distinct peak in cross-correlation functions and ensures more robust delay estimations, especially under low SNR conditions. Experimental results indicate that the improved joint-weighted GCC algorithm achieves an error margin below 1 ns in challenging outdoor electromagnetic scenarios, demonstrating its aptitude for detecting and localizing partial discharge signals in practical engineering applications.

Sound source direction-of-arrival estimation method for microphone array based on ultra-weak fiber Bragg grating distributed acoustic sensor.

A sound source direction-of-arrival (DOA) estimation method for microphone array based on ultra-weak fiber Bragg grating (UW-FBG) distributed acoustic sensor is proposed. The principle of acoustic signal demodulation is introduced, the sound pressure sensitivity and frequency response range of a single UW-FBG microphone are analyzed, and a series linear array with three UW-FBG microphones is designed. Combined with convolutional recurrent neural networks, the DOA estimation method is developed. Log-Mel spectral features and SCOT/PHAT joint weighting generalized cross correlation features are used for DOA estimation. The corresponding system is established and experimentally verified. Results show that the measured sound pressure sensitivity of the UW-FBG microphone is in the range of 0.1032-3.306 rad/Pa within the frequency range of 1000-3000 Hz, and the peak sound pressure sensitivity is about 3.306 rad/Pa. The estimated mean error of 2D DOA estimation is about 2.85°, and the error of 3D DOA estimation is about 5.465°. This method has good application prospects in distributed sound source localization.

Study of Localization of Partial Discharges in Oil-filled Transformers using Acoustic Emission Signals

The measurement of partial discharges (PD) in power transformers is crucial for fault detection and maintenance scheduling. In this paper, the relationship between detection intensity, type of PD source and propagation distance is investigated using an acoustic emission (AE) sensor. The AE wave intensity by the corona discharges are relatively strong. Creeping discharges were next, followed by PD in bubbles. Furthermore, two methods for calculating time difference of arrival (TDOA) in locate calculations, energy reference and generalized cross-correlation (GCC), were experimentally compared. The results showed that the energy reference method is suitable when sensors can be placed around the tank, while the GCC method is suitable when sensors are concentrated in specific parts of the tank. This finding may contribute to improving the accuracy of maintenance diagnostics.

Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

Measurement of vehicle speed based on the GCC algorithm and its application in anti-slip control

Measuring the speed of locomotives is important for train operation, especially in anti-slip control. A speed measurement method is proposed based on the correlation of vibration signals and the core process is to estimate the time shift of the windowed signals using the generalized cross-correlation (GCC) algorithm. A sensitivity analysis of the algorithm and suggested reasonable ranges for critical parameters are conducted. The effectiveness of the proposed method is demonstrated using both simulated and tested vibration signals, showing that it performs well under both constant and variable speed conditions. The proposed method had a maximum improvement in root-mean-square error (RMSE) of 2.72% compared to the measurement of the existing cross-correlation (CC) algorithm. The proposed method is integrated into a heavy-haul train model with an anti-slip controller. Simulation results indicate that the method can accurately measure the locomotive speed when partial or all wheels slip.

Ultra-short baseline positioning delay estimation based on signal preprocessing and fourth-order cumulant

To address the problem that the traditional generalized cross correlation (GCC) method in ultra-short baseline (USBL) positioning systems has a poor delay estimation accuracy in a low signal-to-noise ratio environment or complex noise background, a generalized quadratic cross correlation (GQCC) time delay estimation algorithm based on signal preprocessing and fourth-order cumulants is proposed. The noisy signal was first preprocessed using singular value decomposition and wavelet denoising. Then, the delay was calculated using an algorithm that combined the GQCC and the fourth-order cumulant. The results of the simulation and sea trial demonstrate that the proposed method is superior to the GCC method and the GQCC method and may significantly increase the positioning accuracy of the USBL system. This technique can offer a fresh technological perspective for weak signal detection and passive positioning of small targets in ocean detection.

Polynomial Fitting and Interpolation Method in TDOA Estimation of Sensors Network

Time difference of arrival (TDOA) estimation is the key part of TDOA positioning technology in sensors network, whose result will directly affect the positioning accuracy of the signal source. Aiming at the problem that the accuracy of TDOA estimation is greatly limited by the system sampling frequency, this article proposes an improved TDOA estimation method combining polynomial fitting and interpolation. Based on the classical generalized cross correlation (GCC) method, the peak location of the correlation function is adjusted slightly by polynomial fitting. Furthermore, linear interpolation is used to process the original signal to improve the time resolution between the samples, so as to improve the system sampling frequency indirectly and promote the estimation accuracy to the subsampling level. At the end of the article, the proposed method is validated by simulation and some measured data from a certain place in Lanzhou. The results show that the polynomial fitting method can precisely determine the peak location of the GCC correlation function, especially after interpolating 2–4 points in the original signal, the overall TDOA estimation accuracy can be improved by 0.2%–3.6%, which may provide good reference information for practical engineering application of TDOA positioning technology.

Reaching Long-Term Stability in CP-ϕOTDR

Distributed Acoustic Sensing (DAS) based on coherent reflectometry with chirped pulses (CP-ϕOTDR) is a recent, yet widely recognized technique in providing spatially-resolved distributed measurements of certain physical quantities over the length of an optical fiber. Any given perturbation along the cable can be retrieved as a local temporal shift of the backscattered optical power trace, and recovered using sequential trace-to-trace correlations. However, as a technique that relies on a General Cross-correlation (GCC) estimation function, it displays a non-zero probability of obtaining anomalous estimations, which can fundamentally limit the long-term performance. Furthermore, some demonstrated strategies proposed to mitigate this effect can directly lead to the emergence of cumulative errors, which become as critical as the measurement time increases. Here, we analyze the errors affecting the long-term stability in CP-ϕOTDR systems, and we propose several strategies and processing methods to mitigate their effect. We demonstrate a ∼3 mK error after a one-month continuous temperature experiment performed to a SMF, reaching unprecedented temperature stability in CP-ϕOTDR systems with important implications in emerging fields such as DAS seismology or oceanography.

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms.

Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.

Study of Generalized Phase Spectrum Time Delay Estimation Method for Source Positioning in Small Room Acoustic Environment.

This paper considers the application of signal processing methods to passive indoor positioning with acoustics microphones. The key aspect of this problem is time-delay estimation (TDE) that is used to get the time difference of arrival of the source’s signal between the pair of distributed microphones. This paper studies the approach based on generalized phase spectrum (GPS) TDE methods. These methods use frequency-domain information about the received signals that make them different from widely applied generalized cross-correlation (GCC) methods. Despite the more challenging implementation, GPS TDE methods can be less demanding on computational resources and memory than conventional GCC ones. We propose an algorithmic implementation of a GPS estimator and study the various frequency weighting options in applications to TDE in a small room acoustic environment. The study shows that the GPS method is a reliable option for small acoustically dead rooms and could be effectively applied in presence of moderate in-band noises. However, GPS estimators are far less efficient in less acoustically dead environments, where other TDE options should be considered. The distinguishing feature of the proposed solution is the ability to get the time delay using a limited number of the adjusted bins. The solution could be useful for passively locating moving emitters of narrow-band continual noises using computationally simple frequency detection algorithms.

Deep Audio-Visual Beamforming for Speaker Localization

Generalized Cross Correlation (GCC) is the most popular localization technique over the past decades and can be extended with the beamforming method e.g. Steered Response Power (SRP) when multiple microphone pairs exist. Considering the promising results of Deep Learning (DL) strategies over classical approaches, in this work, instead of directly using Generalized Cross Correlation (GCC), SRP is derived with the DL-learnt ideal correlation functions for each pair of a microphone array. To deploy visual information, we explore the Conditional Variational Auto-Encoder (CVAE) framework in which the audio generative process is conditioned on the visual features encoded by face detections. The vision-derived auxiliary correlation function eventually contributes to the back-end beamformer for improved localization performance. To the best of our knowledge, this is the first deep-generative audiovisual method for speaker localization. Experimental results demonstrate our superior performance over other competitive methods, especially when the speech signal is corrupted by noise.

Acoustic Source Localization Using a Geometrically Sampled Grid SRP-PHAT Algorithm With Max-Pooling Operation

The steered response power phase transform (SRP-PHAT) is a well-known algorithm for acoustic source localization using microphone arrays. It consists in the computation of the generalized cross-correlation (GCC) between each microphone pair, and in the coherent summation of the GCC values in the grid search space. Several improvements based on the volumetric grid have been proposed in order to achieve spatial resolution scalability and to reduce the computational cost by using a coarser grid. In general, the problem of the volumetric based methods is that the noise and the reverberation are projected into the search space since all GCC information is used to build the acoustic map. It is hence proposed a volumetric grid SRP-PHAT algorithm based on the geometrically sampled grid (GSG) that incorporates a max-pooling (MP) operation in the volume accumulation of the GCC values in order to improve the localization performance. The MP is the solution of a minimization-maximization problem that aims at minimizing the deleterious effect of noise and reverberation and at maximizing the accuracy of the GCC values related to the target sound source. Simulations and real-world experiments demonstrate the efficiency of the proposed SRP-GSG-MP algorithm in adverse conditions.

An Analytical Method for 3-D Sound Source Localization Based on a Five-Element Microphone Array

An analytical method for 3D sound source localization based on a five-element microphone array is proposed in this work. With four time-difference-of-arrival (TDOA) values, the proposed method realizes the sound source localization by analytical solution. A new five-element microphone array configuration (which is characterized in that four microphones are in a same plane and at four vertices of a concave quadrilateral respectively, while the fifth microphone is out of the plane) is analyzed and studied. It is proved that with the proposed configuration, the five-element microphone array can obtain a unique and analytical solution. To investigate the effectiveness and the localization performance of the proposed method, a practical five-element microphone array is developed and 3D sound source localization experiments are carried out. The TDOA values are obtained by the generalized cross correlation (GCC) method based on the phase transform (PHAT). The experimental results show that the proposed method is an effective near-field localization method. The localization performance of the practical five-element microphone array is satisfactory. Compared with the conventional methods, the proposed method realizes the 3D localization by analytical solution and has the advantages of simple construction and lower computational complexity.

Cross-related microseismic location based on improved particle swarm optimization and the double-difference location method of jointed coal rock mass

The location algorithm is the core of microseismic monitoring, and the location results directly influence the effectiveness of the subsequent source analysis. We introduced the double-difference (DD) location method in seismic location into microseismic monitoring. We built a travel time equation of the layered velocity model using the ray-tracing theory and analyzed the waveform cross-correlation of the microseismic data using a generalized cross-correlation (GCC) technique. The improved particle swarm algorithm was successfully conducted to improve the randomness of the initial location. We then adopted the DD location method to precisely locate the secondary location, and it showed better location accuracy and computational efficiency than that by a traditional location algorithm based on the simplified geological model. The theoretical simulation and practice results in deep mining show that the proposed method significantly increased the location accuracy, and the results were concentrated and consistent with the field results. Moreover, we verified the importance of the velocity model for location accuracy. This algorithm also increased the ease of identifying fracture regions, providing the necessary theoretical and technical support for the source location of the jointed coal rock mass in deep mining.

High resolution time of arrival estimation algorithm for B5G indoor positioning

In beyond 5G (B5G) indoor positioning time of arrival (TOA) positioning method is an important one. Base stations measure reference signals sent by terminals to calculate the TOA. The accuracy of position calculation is highly dependent on the TOA estimation resolution. This paper proposes a high-resolution TOA estimation algorithm based on Sounding Reference Signal (SRS) of 5G communication system. The algorithm achieves high resolution estimation by two steps. The Least Mean Square (LMS) adaptive filtering method is used as the first step to obtain the integer part of TOA, which is a coarse estimation of TOA. In the second step, we use weighted Generalized Cross-Correlation (GCC) algorithm to obtain the fractional part of TOA in frequency domain to get a more precise TOA estimation. Moreover, the stability of the estimated results is improved by the threshold value setting. The proposed algorithm is simulated in both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. The simulation results show that the TOA error achieves 0.56 m in AWGN and 0.96 m in Rayleigh fading channels for 80% of test samples respectively.

A modified model predictive control method for frequency regulation of microgrids under status feedback attacks and time-delay attacks

As a promising technology to integrate renewable energy and enable decentralized energy management, microgrid (MG) offers an appealing network architecture due to its potential economic, environmental, and technical benefits. However, with the increasing deployment of intelligent devices and the growing network interconnectivity, communication channels and controllers for MGs become more vulnerable to emerging cyber threats. Two types of attacks are considered in this paper, including the data integrity attacks on the system status feedback and time-delay attacks which may disrupt the control of the MGs and lead to adverse consequences. A modified model predictive control (MPC) scheme is proposed for the secondary frequency control of MGs based on the online status switching method and generalized cross correlation (GCC) estimation to detect the real system status and time delay injected to the control system. The Euclidean metric is used in the online status switching method to obtain the real system states. Meanwhile, the GCC based delay estimation is developed to detect and track the time delay posed by attacks in the real-time operation. Case studies under different scenarios of the attacks are conducted, and the simulation results verify the effectiveness of the proposed MPC scheme under the cyberattacks.

Noise reduction in infrasound signals based on mask coefficient binary weighting – Generalized cross correlation – Non-negative matrix factorization algorithm

Infrasound noise reduction has two difficulties: the extremely limited datasets and the overlap of frequency bands. To overcome these problems, we present an algorithm, named MCWGCC-NMF, for noise reduction in multi-sensor recordings of mixture infrasound signals. The method is based on non-negative matrix factorization (NMF), and it is combined with spatial information estimated by generalized cross correlation (GCC) and mask coefficient binary weighting (MCW). The source dictionary masking by GCC-NMF, which uses spatial information of individual NMF atoms, is performed by frequency weighting and threshold selection to alleviate the frequency overlap. The results show that the proposed method achieves consistently better performance than the GCC-NMF on the infrasound dataset. Our study provides a signal processing method of infrasound noise reduction, which has the potential to further enhance the signals reception of infrasound arrays with wind noise reduction system (WNRS).