Line Spectrum Detection Algorithm of Single Vector Sensor Based on Singular Value Difference
Source abstract
Abstract
The line spectrum of ship-radiated noise can be used in passive sonar to detect ship targets; however, it is affected by ocean noise and the Doppler effect, which has a negative effect on target detection. In the paper, the multi-channel data of a single vector sensor is used, and the line spectrum detection algorithm based on singular value difference in frequency domain is proposed for detecting ship targets. The simulated and experimental results show that the proposed algorithm can be used to detect line spectrum under Doppler and background noise interference. The proposed algorithm can provide feasible and low-cost line spectrum detection results in engineering applications, which ensures the certainty of passive detection by using this method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radar Systems and Signal Processing · Advanced SAR Imaging Techniques · Direction-of-Arrival Estimation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Intelligent System Science and Information Engineering, Shenyang University, Shenyang 110044, China
Dalian Measurement and Control Technology Research Institute, Dalian 116000, China
Normal School, Shenyang University, Shenyang 110044, China
Funding
Key Laboratory Fund Item of China · PhD Research Startup Foundation project of Liaoning Province of China
References (33)
Show source-provided references
1. A Space-Frequency Joint Detection and Tracking Method for Line-Spectrum Components of Underwater Acoustic Signals10.1016/j.apacoust.2020.107609 ↗
2. Deep Learning Classification for Improved Bicoherence Feature Based on Cyclic Modulation And Cross-Correlation10.1121/1.5127166 ↗
3. The Underwater Acoustic Target Timbre Perception and Recognition Based on the Auditory Inspired Deep Convolutional Neural Network10.1016/j.apacoust.2021.108210 ↗
4. Modulation Recognition of Underwater Acoustic Communication Signals Based on Deep Learning10.1186/s13634-024-01200-9 ↗
5. Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency10.1109/ACCESS.2021.3113042 ↗
6. Line Spectrum Trajectory Extraction Method of Underwater Acoustic Signal Based on Dynamic Parameter HMM
7. Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations10.3390/s20061767 ↗
8. Wang, M., Qiu, B.C., Zhu, Z.F., Ma, L., and Zhou, C.P. (2022). Passive Tracking of Underwater Acoustic Targets Based on Multi-Beam Lofar and Deep Learning. PLoS ONE, 17.10.1371/journal.pone.0273898 ↗
9. Suppressing Short Time Marine Ambient Noise Based on Deep Complex Unet to Enhance the Vessel Radiation Signal in Lofar Spectrogram10.1016/j.jappgeo.2024.105611 ↗
10. A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning10.3390/jmse11081632 ↗
11. Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression10.1007/s13369-024-09073-8 ↗
12. Underwater Acoustic Signal Denoising Based on Sparse Tqwt and Wavelet Thresholding10.1016/j.dsp.2024.104601 ↗
13. A New Method for Denoising Underwater Acoustic Signals Based On EEMD, Correlation Coefficient, Permutation Entropy, and Wavelet Threshold Denoising10.1007/s11804-024-00386-6 ↗
14. Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error10.1007/s13369-024-09682-3 ↗
15. Yu, X.Y., Chi, C., Li, S.Q., Li, Y., Huang, H.N., and Wang, G.Q. (2024, January 15–18). Underwater Acoustic Target Recognition via Combining Adaptive Line Enhancement and Deep Neural Network. Proceedings of the 2024 International Conference on Oceans, Singapore.10.1109/OCEANS51537.2024.10682283 ↗
16. Underwater Delay Estimation Based on Adaptive Singular Value Decomposition Reconstruction Under Low SNR and Multipath Conditions10.1002/ett.70145 ↗
17. Underwater Acoustic Signal Denoising Method Based on DBO-VMD and Singular Value Decomposition10.1063/5.0272423 ↗
18. Line Spectrum Detection Algorithm Based on the Phase Feature of Target Radiated Noise
19. Wang, P., Dong, J., Wang, L.F., and Qiao, S.H. (2024). Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones. Micromachines, 15.10.3390/mi15101183 ↗
20. Passive Bearing Estimation Using a 2-D Acoustic Vector Sensor Mounted on a Hybrid Autonomous Underwater Vehicle10.1109/JOE.2021.3132647 ↗
21. Experimental Study on Performance Improvement of Underwater Acoustic Communication Using a Single Vector Sensor10.1109/JOE.2024.3374424 ↗
22. Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing10.3390/electronics13091796 ↗
23. Acoustic vector-sensor correlations in ambient noise10.1109/48.946508 ↗
24. Zhang, W.X., Chen, Y., Bian, Q., Liu, Y.Y., Liang, Y., and Meng, Z. (2025). An Adaptive Dual-Channel Underwater Target Detection Method Based on a Vector Cross-Trispectrum Diagonal Slice. J. Mar. Sci. Eng., 13.10.3390/jmse13091628 ↗
25. Application research on vector coherent frequency-domain batch adaptive line enhancement in deep water10.1049/rsn2.12621 ↗
26. Shchurov, V.A. (2022). Movement of Acoustic Energy in the Ocean, Springer.10.1007/978-981-19-1300-6 ↗
27. Ali, S.S., Zhao, W., Jin, M., and Yoo, S.J. (2019, January 16–18). Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. Proceedings of the 2019 International Conference on Information and Communication Technology Convergence (ICTC), Jeju-si, Republic of Korea.10.1109/ICTC46691.2019.8939923 ↗
28. Spectrum Sensing Algorithms via Finite Random Matrices10.1109/TCOMM.2011.112311.100721 ↗
29. Cooperative Spectrum Sensing Based on The Limiting Eigenvalue Ratio Distribution in Wishart Matrices10.1109/LCOMM.2009.090425 ↗
30. Multiantenna-Assisted Spectrum Sensing for Cognitive Radio10.1109/TVT.2009.2037912 ↗
31. Multi-Antenna Based Spectrum Sensing for Cognitive Radios: A GLRT Approach10.1109/TCOMM.2010.01.080158 ↗
32. Studentized Extreme Eigenvalue Based Double Threshold Spectrum Sensing Under Noise Uncertainty
33. Eigenvalue-based spectrum sensing algorithms for cognitive radio10.1109/TCOMM.2009.06.070402 ↗