Sea Clutter Mitigation Using Resonance-Based Signal Decomposition

Nguyen, Si Tran Nguyen; Al-Ashwal, Waddah A.

doi:10.1109/lgrs.2015.2464807

Cited by 25 publications

(15 citation statements)

References 17 publications

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“…The total amount of subbanks is + 1. Signal of each filter bank is output by highpass filter, and signal of the final filter bank is output by lowpass filter [24][25][26].…”

Section: Tunable Q-factor Wavelet Transformmentioning

confidence: 99%

“…Hilbert marginal spectrum analysis is based on HHT which comprises empirical mode decomposition (EMD) and the Hilbert spectral analysis [26]. As a crucial part of HHT, EMD is a creative technique of processing nonlinear and nonstationary signals.…”

Section: Hilbert Marginal Spectrum Analysismentioning

confidence: 99%

“…For the high resonance signal hr( ) obtained by RSSD, the process of obtaining the IMFs from hr( ) is performed along with the following steps [26]:…”

Section: Hilbert Marginal Spectrum Analysismentioning

confidence: 99%

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Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

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“…The total amount of subbanks is + 1. Signal of each filter bank is output by highpass filter, and signal of the final filter bank is output by lowpass filter [24][25][26].…”

Section: Tunable Q-factor Wavelet Transformmentioning

confidence: 99%

Section: Hilbert Marginal Spectrum Analysismentioning

confidence: 99%

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Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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“…The application of sparse signal separation in the maritime domain is motivated by the fact that the sea clutter Doppler spectrum has complex interactions between the large gravity waves, small capillary waves and the wind which results in a broad Doppler spectrum (low Q), whilst the target component is assumed to have approximately constant radial velocity (narrow Doppler spectrum 1 and high Q transform) [5]. Hence, if we let Q 1 and Q 2 be the high and low Q factors, the sea clutter and target components can be extracted using the appropriate TQWT transforms.…”

Section: Resonance Based Separationmentioning

confidence: 99%

“…For example, resonance-based decomposition using dual tuned Q wavelet transforms (TQWT) has been proposed for mitigating sea-clutter from the backscatter while leaving the target returns mostly unperturbed [5]. This approach was applied to a real data set and yielded improvements in the detection performance over the unprocessed data.…”

Section: Introductionmentioning

confidence: 99%

Target detection in sea clutter using resonance based signal decomposition

Rosenberg

Nguyen

2016

2016 IEEE Radar Conference (RadarConf)

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Detection in the maritime domain requires the radar return from an object of interest (target) to be distinguishable from the background interference (sea clutter and noise). These radars traditionally use non-coherent processing methods due to the time-varying and range-varying nature of the Doppler spectra. However, the radar backscatter may contain sea-spikes which can last for seconds and resemble targets. In this paper, a method of sparse signal separation known as 'basis pursuit denoising' has been applied to two different sea clutter data sets to demonstrate the potential for improved detection performance. We also explore how the parameters required for this technique can be selected depending on the target velocity.

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