Underwater Radiated Signal Analysis in the Modulation Spectrogram Domain

Cho, Hyunjin; Lim, Jun‐Seok; Ku, Bonhwa; Cheong, MyoungJun; Seo, Iksu; Ko, Hanseok; Hong, Wooyoung

doi:10.1587/transfun.e98.a.1751

Cited by 4 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider the practical scenarios where the tonal signal dominates the received signal and is used in detecting a marine object. The signal r(t) detected by the passive sonar is expressed as [5,6,16]…”

Section: Signal Modelmentioning

confidence: 99%

“…where A i ∈ C is the (complex) amplitude of the ith frequency component [16]. Also, the propeller noise is given by [6] r…”

Section: Signal Modelmentioning

confidence: 99%

“…In order to prevent the aliasing phenomenon, we set the cutoff frequency f c of the LPF to be smaller than f s /α where α is the decimation ratio. Since the desired tonal signal consists of low frequency components [6], we need to preserve the tonal component yet filter out unwanted high frequency interferences and noise. Using the low-pass filter with the cutoff frequency f c and decimation with the decimation ratio α (α > 1), the output of LPF and decimation process r l at the lth channel is given by…”

Section: Noise Suppression Via Pre-filteringmentioning

confidence: 99%

“…1). Among them, the tonal signal, the sum of sinusoidal tones occurred by a marine object [6], has received special attention as a tool to identify the marine object. Since each object can be characterized by the frequency dependent features, accurate identification of the frequency components in a tonal signal is of great importance for the target object detection.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tonal signal detection in passive sonar systems using atomic norm minimization

Kim

Nguyen

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the frequency domain is unavoidable, resulting in a severe degradation of the object detection quality. To overcome the basis mismatch error, we propose a tonal frequency estimation technique in the continuous frequency domain. Towards this end, we formulate the frequency estimation problem as an atomic norm minimization problem. From the numerical experiments, we show that the proposed technique is effective in identifying the tonal frequency components of marine objects.

show abstract

Section: Signal Modelmentioning

confidence: 99%

“…where A i ∈ C is the (complex) amplitude of the ith frequency component [16]. Also, the propeller noise is given by [6] r…”

Section: Signal Modelmentioning

confidence: 99%

Section: Noise Suppression Via Pre-filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tonal signal detection in passive sonar systems using atomic norm minimization

Kim

Nguyen

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The signals are typically composed of narrowband and broadband components. In particular, the frequency estimation of the tonal components (extremely narrowband components) is an important issue for detecting and identifying marine objects [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

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

Shin

Hong

Lee

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract