Acoustic Source Localization Based on Ultra-Narrow Band Modal Decomposition under Nonstationary Operating Conditions
Source abstract
Abstract
Acoustic source localization plays a vital role in fault diagnosis, and time difference of arrival (TDoA) is an important acoustic source localization method. In engineering practice, non-stationary working conditions and strong noise background bring challenges to TDoA and fault identification. In this paper, a novel acoustic source localization method called ultra-narrow band modal decomposition (UNBMD) based TDoA is proposed. First, frequency-domain windows corresponding to characteristic modes is constructed to enhance the decomposition accuracy of signal under strong noise. Second, correlation analysis is utilized to obtain accurate time delays for each channel. In the simulation studies, signal-to-noise ratio (SNR) and waveform similarity are adopted as evaluation metrics. Comparative analysis with variational mode decomposition and band-pass filtering methods demonstrates that the proposed approach achieves a waveform similarity of 99.86% and an SNR of 25.3427 dB, thereby exhibiting superior performance. In engineering verification, an experimental study was conducted on actual tobacco machinery. The results indicate that the proposed UNBMD-TDoA method can realize the acoustic source localization under strong noise conditions, offering a reliable technical foundation for the fault diagnosis and maintenance of mechanical systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Speech and Audio Processing · Aerodynamics and Acoustics in Jet Flows · Speech Recognition and Synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (22)
Show source-provided references
1. Jiang, W., Wu, J., Yang, Y., Li, X. and Zhu, H., Health evaluation techniques towards rotating machinery: A systematic literature review and implementation guideline, Reliab. Eng. Syst. Saf., 2025, vol. 260, p. 110924.10.1016/j.ress.2025.110924 ↗
2. Guo, Z., Du, W., Li, C., Guo, X. and Liu, Z., Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest, Measurement, 2025, vol. 248, p. 116936.10.1016/j.measurement.2025.116936 ↗
3. Tian, A., Zhang, Y., Ma, C., Chen, H., Sheng, W. and Zhou, S., Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain, Measurement, 2023, vol. 207, p. 112327.10.1016/j.measurement.2022.112327 ↗
4. Kirmaz, A., Şahin, T., Michalopoulos, D.S. and Gerstacker, W., ToA and TDoA estimation using artificial neural networks for high-accuracy ranging, IEEE J. Sel. Areas Commun., 2023, vol. 41, pp. 3816–3830.10.1109/JSAC.2023.3322803 ↗
5. Gong, Z., Li, C. and Su, R., Fundamental limits of Doppler Shift-Based, ToA-Based, and TDoA-based underwater localization, IEEE/CAA J. Autom. Sin., 2023, vol. 10, pp. 1637–1639.
6. Delcourt, M. and Boudec, J.Y.L., TDoA source-localization technique robust to time-synchronization attacks, IEEE Trans. Inf. Forensics Secur., 2021, vol. 16, pp. 4249–4264.10.1109/TIFS.2020.3001741 ↗
7. Wang, Y., Liang, X., Zhang, J., Ren, B. and Jin, K., Robust TDoA/FDOA estimation from emitter signals for hybrid localization using UAVs, Def. Technol., 2022, vol. 18, pp. 81–93.10.1016/j.dt.2020.11.018 ↗
8. Tu, C., Cui, X., Liu, G., Zhao, S. and Lu, M., Parameterized TDoA: TDoA estimation for mobile target localization in a time-division broadcast positioning system, IEEE Internet Things J., 2025, vol. 12, pp. 24131–24147.10.1109/JIOT.2025.3554528 ↗
9. Wang, W., Wang, G., Ho, K.C. and Huang, L., Robust TDoA localization based on maximum correntropy criterion with variable center, Signal Process., 2023, vol. 205, p. 108860.10.1016/j.sigpro.2022.108860 ↗
10. Bottigliero, S., Milanesio, D., Saccani, M. and Maggiora, R., A Low-cost indoor real-time locating system based on TDoA estimation of UWB pulse sequences, IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–11.10.1109/TIM.2021.3069486 ↗
11. Ma, T., Zhang, D., Zhuang, J., You, C., Yin, G. and Tang, S., TDoA-based UWB indoor 1D localization via weighted sliding window filtering, Digit. Signal Process., 2024, vol. 151, p. 104544.10.1016/j.dsp.2024.104544 ↗
12. Cui, J., Yuan, Z. and Liang, H., Spatial distribution characteristics of positioning accuracy in 3D-TDoA Localization: analysis and applications, IEEE Internet Things J., 2025, vol. 12, pp. 30114–30127.10.1109/JIOT.2025.3570802 ↗
13. Li, J., Li, Y., Jiang, H., Zhang, X. and Wu, Q., Multi-TDoA estimation and source direct position determination based on parallel factor analysis, IEEE Internet Things J., 2023, vol. 10, pp. 7405–7415.10.1109/JIOT.2022.3228810 ↗
14. Terças, L., Alves, H., Lima, C.H.M.d. and Juntti, M., Bayesian-based indoor factory positioning using AOA, TDoA, and Hybrid Measurements, IEEE Internet Things J., 2024, vol. 11, pp. 21620–21631.10.1109/JIOT.2024.3374457 ↗
15. Li, H., Elnahas, O. and Quan, Z., TDoA-based indoor localization via linear fusion with low-rank matrix approximation, IEEE Internet Things J., 2024, vol. 11, pp. 10635–10647.10.1109/JIOT.2023.3327883 ↗
16. Kang, X., Wang, D., Shao, Y., Ma, M. and Zhang, T., An Efficient hybrid multi-station TDoA and single-station AOA localization method, IEEE Trans. Wireless Commun., 2023, vol. 22, pp. 5657–5670.10.1109/TWC.2023.3235753 ↗
17. Ho, K. and Le, T., Integrating AOA with TDoA for joint source and sensor localization, IEEE Trans. Signal Process., 2023, vol. 71, pp. 2087–2102.10.1109/TSP.2023.3280417 ↗
18. Nyantakyi, I.O., Wan, Q., Ni, L., Mensah, E.O. and Bamisile, O., ACGA: Adaptive Conjugate Gradient Algorithm for non-line-of-sight hybrid TDoA-AOA localization, Measurement, 2024, vol. 224, p. 113820.10.1016/j.measurement.2023.113820 ↗
19. Zhang, Z., Chen, W., Wu, K., Liu, H., Chen, X., Jiang, T., Ma, Z. and Feng, W., Novel approach for partial discharge localization based on fiber-optic F‑P Sensing array and modified TDoA in a 110-kV Transformer, IEEE Trans. Instrum. Meas., 2024, vol. 73, pp. 1–11.
20. Li, Q., Lai, Y. and Cao, D., Investigating electromagnetic acoustic emission signals denoising for alloy materials nondestructive testing: A CRQA Method, Russ. J. Nondestr. Test., 2024, vol. 60, pp. 144–156.10.1134/S1061830923601393 ↗
21. Wang, Y., Ho, K.C. and Wang, Z., Robust localization under NLOS environment in the presence of isolated outliers by full-Set TDoA measurements, Signal Process., 2023, vol. 212, p. 109159.10.1016/j.sigpro.2023.109159 ↗
22. Wei, M.H., Jiang, L.X., Zhang, D., Wang, B., Tu, F.M. and Jiang, P.B., Bearing fault feature extraction method based on variational mode decomposition of fractional Fourier transform, Russ. J. Nondestr. Test., 2022, vol. 58, pp. 221–235.10.1134/S1061830922030056 ↗