Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Villadangos, J.M.; Ureña, J.; Domínguez, Juan Jesús García; Martín, Ana Jiménez; Hernández, Álvaro; Perez-Rubio, Ma Carmen

doi:10.3390/s21217051

Cited by 5 publications

(3 citation statements)

References 28 publications

(44 reference statements)

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“…Generalized cross correlation (GCC) [15][16][17] is a classical approach for TDE, it applies a weighting function to the cross-power spectrum to improve the signal-to-noise ratio (SNR) and thus sharpen the peak of the correlation function. In various GCC methods, the GCC with phase transform (GCC-PHAT) method can make the TDE robust against noise and reverberation and thus has better performance than others [18][19][20]. On the strength of the GCC, some new methods have been proposed for high-precision TDE.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

Li¹,

Jiang²,

Yu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other state-of-the-art methods, the proposed method significantly reduces the computational complexity and achieves better estimation performance. Experiments on both simulation and real-world data verify the efficiency of the proposed approach. KEYWORDSTime delay estimation (TDE); time difference of arrival (TDOA); taylor expansion; super-resolution

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Section: Introductionmentioning

confidence: 99%

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

Li¹,

Jiang²,

Yu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…There are three main methods of sound source location: TDE or TDOA, depth learning, and sound energy First of all, the method based on TDE or TDOA is to calculate the time delay or phase difference of sound arriving at different acoustic sensors, and estimate the arrival azimuth of the sound source under the condition that the geometry of the array is known. A typical algorithm is phase transformation (GCC-PHAT) (Peŕez-Rubio, 2021;Yoshizawa, 2021), This kind of method is limited by time synchronization, and has poor practicability and high cost.…”

Section: Introductionmentioning

confidence: 99%

Recognition and location of marine animal sounds using two-stream ConvNet with attention

Hou

Liao

et al. 2023

Front. Mar. Sci.

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There are abundant resources and many endangered marine animals in the ocean. Using sound to effectively identify and locate them, and estimate their distribution area, has a very important role in the study of the complex diversity of marine animals (Hanny et al., 2013). We design a Two-Stream ConvNet with Attention (TSCA) model, which is a two-stream model combined with attention, in which one branch processes the temporal signal and the other branch processes the frequency domain signal; It makes good use of the characteristics of high time resolution of time domain signal and high recognition rate of frequency domain signal features of sound, and it realizes rapid localization and recognition of sound of marine species. The basic network architecture of the model is YOLO (You Only Look Once) (Joseph et al., 2016). A new loss function focal loss is constructed to strengthen the impact on the tail class of the sample, overcome the problem of data imbalance and avoid over fitting. At the same time, the attention module is constructed to focus on more detailed sound features, so as to improve the noise resistance of the model and achieve high-precision marine species identification and location. In The Watkins Marine Mammal Sound Database, the recognition rate of the algorithm reached 92.04% and the positioning accuracy reached 78.4%.The experimental results show that the algorithm has good robustness, high recognition accuracy and positioning accuracy.

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“…The GCC-PHAT weighting method [ 12 , 33 , 34 , 35 , 36 ] can achieve better delay estimation results when the signal-to-noise ratio is high and the reverberation is weak. This leads to false peaks and increases the delay estimation error.…”

Section: Theoretical Introduction Of Delay Estimation Optimization Al...mentioning

confidence: 99%

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved GCC-PHAT Weighting

Wang

et al. 2022

Sensors

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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-ργ 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-ργ 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-ργ weighting (meaning: GCC-PHAT-ργ weighting based on singular value decomposition). The results show that the delay estimation optimization algorithm based on SVD-GCC-PHAT-ργ 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.

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Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Cited by 5 publications

References 28 publications

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

Recognition and location of marine animal sounds using two-stream ConvNet with attention

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved GCC-PHAT Weighting

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