Line Spectrum Trajectory Extraction Method of Underwater Acoustic Signal Based on Dynamic Parameter HMM

LUO Xinwei; LI Lei; SHEN Zihan

doi:10.11999/JEIT211374

Volume 44 Issue 6

Jun. 2022

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LUO Xinwei, LI Lei, SHEN Zihan. Line Spectrum Trajectory Extraction Method of Underwater Acoustic Signal Based on Dynamic Parameter HMM[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1956-1965. doi: 10.11999/JEIT211374

Citation:

LUO Xinwei, LI Lei, SHEN Zihan. Line Spectrum Trajectory Extraction Method of Underwater Acoustic Signal Based on Dynamic Parameter HMM[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1956-1965. doi: 10.11999/JEIT211374

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Line Spectrum Trajectory Extraction Method of Underwater Acoustic Signal Based on Dynamic Parameter HMM

doi: 10.11999/JEIT211374

LUO Xinwei^{1
,
,},
LI Lei¹,
SHEN Zihan²

1.
Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China
2.
Shenyang Aircraft Design and Research Institute, Shenyang 110035, China

Funds: The National Natural Science Foundation of China (12174053, 91938203), The Fundamental Research Funds for the Central Universities (2242021k30019)

Received Date: 2021-11-30
Accepted Date: 2022-03-22
Rev Recd Date: 2022-03-12

Available Online: 2022-03-26

Publish Date: 2022-06-21

Abstract

Abstract

In view of the weak ability of traditional Hidden Markov model (HMM) method to extract time-varying line spectrum and multi line spectrum and the large amount of calculation in dynamic programming process, a One- Dimensional Hidden Markov Model (1D-HMM) method based on dynamic parameters is proposed to extract line spectrum trajectory in LOw Frequency Analysis and Recording (LOFAR) diagram of underwater acoustic signal. In this method, the time-varying frequency state is modeled as a first-order Markov process, and the Viterbi algorithm is repeated several times to extract multiple frequency trajectories with the largest a posteriori probability. In the iterative process, the state transition probability matrix in HMM is dynamically adjusted by the first derivative of the sequence calculated in real time, which improves the extraction ability of line spectrum trajectory and the resolution ability of multi line spectrum; A power spectrum accumulation method based on dynamic sliding window is designed to estimate the birth and death of line spectrum and eliminate false line spectrum trajectories. At the same time, the block processing strategy is designed for LOFAR graph data in the implementation process, which reduces greatly the amount of calculation. The simulation and actual data processing results show that the method can effectively detect and track the frequency state of complex time-varying spectrum with good operational efficiency under low signal-to-noise ratio conditions, which provides good technical support for the detection of weak signals in sonar devices.
- Line spectrum detection and tracking,
- Frequency trajectory,
- Hidden Markov Model (HMM),
- Dynamic search

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References(20)

References

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