scholarly journals A Sensing and Tracking Algorithm for Multiple Frequency Line Components in Underwater Acoustic Signals

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4866 ◽  
Author(s):  
Xinwei Luo ◽  
Zihan Shen
Keyword(s):  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Multiple Time ◽  
Time Varying ◽  
Multiple Frequency ◽  
Underwater Acoustic ◽  
Model Method ◽  
Detection And Tracking ◽  
Time Varying Frequency ◽  
Line Tracking

Reliable and efficient sensing and tracking of multiple weak or time-varying frequency line components in underwater acoustic signals is the topic of this paper. We propose a method for automatic detection and tracking of multiple frequency lines in lofargram based on hidden Markov model (HMM). Instead of being directly subjected to frequency line tracking, the whole lofargram is first segmented into several sub-lofargrams. Then, the sub-lofargrams suspected to contain frequency lines are screened. In these sub-lofargrams, the HMM-based method is used for detection of multiple frequency lines. Using image stitching and statistical model method, the frequency lines with overlapping parts detected by different sub-lofargrams are merged to obtain the final detection results. The method can effectively detect multiple time-varying frequency lines of underwater acoustic signals while ensuring the performance under the condition of low signal-to-noise ratio (SNR). It can be concluded that the proposed algorithm can provide better multiple frequency lines sensing ability while greatly reducing the amount of calculations and providing potential techniques for feature sensing and tracking processing of unattended equipment such as sonar buoys and submerged buoys.

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 11 ◽  
Author(s):  
Guohui Li ◽  
Qianru Guan ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Ensemble Empirical Mode Decomposition ◽  
Underwater Acoustic ◽  
Real Signal ◽  
Mode Decomposition ◽  
Soft Threshold

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

scholarly journals Locating Ships Using Time Reversal and Matrix Pencil Method by Their Underwater Acoustic Signals

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5065
Author(s):  
Daniel Chaparro-Arce ◽  
Sergio Gutierrez ◽  
Andres Gallego ◽  
Cesar Pedraza ◽  
Felix Vega ◽  
...  
Keyword(s):  
Time Reversal ◽  
Communication Channel ◽  
Matrix Pencil ◽  
Acoustic Signals ◽  
Underwater Acoustic ◽  
Low Bit Rate ◽  
Matrix Pencil Method ◽  
The Matrix ◽  
Resonance Expansion ◽  
Passive Sensors

This paper presents a technique, based on the matrix pencil method (MPM), for the compression of underwater acoustic signals produced by boat engines. The compressed signal, represented by its complex resonance expansion, is intended to be sent over a low-bit-rate wireless communication channel. We demonstrate that the method can provide data compression greater than 60%, ensuring a correlation greater than 93% between the reconstructed and the original signal, at a sampling frequency of 2.2 kHz. Once the signal was reconstituted, a localization process was carried out with the time reversal method (TR) using information from four different sensors in a simulation environment. This process sought to achieve the identification of the position of the ship using only passive sensors, considering two different sensor arrangements.

scholarly journals Study on Time Reversal Maximum Ratio Combining in Underwater Acoustic Communications

2021 ◽  
Vol 11 (4) ◽  
pp. 1509
Author(s):  
Anbang Zhao ◽  
Caigao Zeng ◽  
Juan Hui ◽  
Keren Wang ◽  
Kaiyu Tang
Keyword(s):  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Spatial Diversity ◽  
Single Element ◽  
Noise Power ◽  
Theoretical Derivation ◽  
Underwater Acoustic ◽  
Maximum Ratio Combining ◽  
Weight Coefficients ◽  
Output Snr

Time reversal (TR) can achieve temporal and spatial focusing by exploiting spatial diversity in complex underwater environments with significant multipath. This property makes TR useful for underwater acoustic (UWA) communications. Conventional TR is realized by performing equal gain combining (EGC) on the single element TR output signals of each element of the vertical receive array (VRA). However, in the actual environment, the signal-to-noise ratio (SNR) and the received noise power of each element are different, which leads to the reduction of the focusing gain. This paper proposes a time reversal maximum ratio combining (TR-MRC) method to process the received signals of the VRA, so that a higher output SNR can be obtained. The theoretical derivation of the TR-MRC weight coefficients indicates that the weight coefficients are only related to the input noise power of each element, and are not affected by the multipath structure. The correctness of the derivation is demonstrated with the experimental data of the long-range UWA communications conducted in the South China Sea. In addition, the experimental results illustrate that compared to the conventional TR, TR-MRC can provide better performance in terms of output SNR and bit error rate (BER) in UWA communications.

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