Tonal signal detection in passive sonar systems using atomic norm minimization

Kim, Jin Hong; Kim, Junhan; Nguyen, Luong Trung; Shim, Byonghyo; Hong, Wooyoung

doi:10.1186/s13634-019-0641-5

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…The signal that is received by the passive sonar system can be classified into four signal types as follows: tonal signals, propeller noise, hydrodynamic noise, and ambient noise [ 21 , 40 ]. The tonal signals, which are generated by the machinery component of marine objects, are generally present at low-frequency bands.…”

Section: Signal Model For the Frequency Analysismentioning

confidence: 99%

“…When using the SBL for the frequency estimation, the frequency difference between adjacent columns is set as 1 Hz in order to improve the frequency resolution with the same measurements, and it induces the underdetermined linear system of Equation (4) as expected. The zero-mean white Gaussian noise

is added to clean signal

, and the corresponding SNR is computed as follows [ 21 , 24 , 33 , 34 ]: SNR

.…”

Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 99%

“…Meanwhile, the conventional CS has a basis mismatch problem because the true values in the continuous domain are expressed using the sparse representation in the discrete domain. Atomic norm minimization (ANM) is adopted in order to mitigate the problem [ 19 , 20 , 21 ]. However, it requires a computational burden with respect to the linear system size.…”

Section: Introductionmentioning

confidence: 99%

“…Both conventional and advanced CS suffers from manually determined regularization parameters controlling the sparsity of the solution [ 6 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In order to circumvent the limitation of CS, sparse Bayesian learning (SBL) has been applied in underwater acoustics.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

Shin

Hong

Lee

et al. 2021

Sensors

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Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components.

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Section: Signal Model For the Frequency Analysismentioning

confidence: 99%

is added to clean signal

, and the corresponding SNR is computed as follows [ 21 , 24 , 33 , 34 ]: SNR

.…”

Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

Shin

Hong

Lee

et al. 2021

Sensors

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“…However, the fast and accurate acquisition of frequency is difficult due to the lack of the direct detection of frequency in many applications. Therefore, a number of researchers in the above fields have turned their attention to frequency estimation for frequency acquisition [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An enhanced adaptive notch filtering method for online multi-frequency estimation from contaminated signals of a mechanical control system

Liu¹,

Zhang²,

Yin³

et al. 2021

Meas. Sci. Technol.

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Real-time frequency information is extremely important in many fields of mechanical engineering, such as fault diagnosis, noise and vibration control, underwater acoustic detection, vehicle communication, etc. However, sometimes frequencies cannot be directly detected, making it important to quickly and accurately estimate the frequencies from contaminated signals of a mechanical system. An adaptive notch filter (ANF) is one of the most popular methods for online frequency estimation due to its simple structure and low computational complexity. However, ANF is a biased estimation if the signal contains uncorrelated noise. An enhanced adaptive notch filtering (EANF) method, which is able to reduce the frequency estimation bias and improve the estimation speed from contaminated signals, is proposed in this paper. Firstly, the limitations of the traditional ANF method are theoretically and numerically analyzed. Then, the principles of the proposed EANF method are formulated, including key parameter optimization and uncorrelated noise compensation in the update process. Afterwards, a multiple extension of the proposed EANF method is constructed using the adaptive simultaneous structure. The results of the numerical simulation show that the proposed method is superior to traditional ones. Finally, a two-stage vibration isolation system is established for experimental validation. The experimental results also demonstrate the effectiveness of the proposed method.

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