Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter

In this study, two helicopter classification methods with a high pulse repetition frequency radar are proposed. By analysing the returned signal from a main rotor, the authors find that the essential difference between rotors appears when none of blades is orthogonal to the incident waves. Based on this difference, the authors propose two methods to classify the helicopter. The first one is based on the matched filters (MFs), including the time MF and the time-frequency MF. The second one derives from time-frequency masks. Experimental results demonstrate that both methods have the ability to classify helicopters with the same micro-Doppler parameters at a low signal-to-noise ratio. And they are robust against errors of the estimated parameters and the gesture of the helicopter.

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“…An efficient Cohen's class distribution named S-method (SM) is proposed in [18,19], which is obtained directly from the STFT…”

Section: Classification Results With Accurately Estimated Parametersmentioning

confidence: 99%

Two helicopter classification methods with a high pulse repetition frequency radar

Zuo

Zhang

et al. 2013

IET Radar, Sonar & Navigation

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“…Irrespective of the value of N, the ranks and number of non-zero eigenvalues of H x N and H x l N cannot be greater than 2L and 2 respectively [22,23], where x½n and x l ½n are given by Eq. (3). The trace of the matrix can be expressed in terms of its non-zero eigenvalues as follows [21]:…”

Section: Extraction Of Components From a Multi-component Signal With mentioning

confidence: 99%

“…Many signals encountered in practice, such as speech signal, electroencephalogram (EEG) signal, seismic signal, and electrocardiogram (ECG) signal are multi-component non-stationary signals. The decomposition of a multi-component signal into mono-component signals finds use in signal analysis [2][3][4], modeling and classification [5][6][7]. The Hilbert transform and discrete-energy separation algorithm (DESA) can be applied to extracted mono-component signals to compute their instantaneous frequencies [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An iterative approach for decomposition of multi-component non-stationary signals based on eigenvalue decomposition of the Hankel matrix

Jain

Pachori

2015

Journal of the Franklin Institute

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“…The WVD(t, f ) can be computed by the short-time Fourier transform (STFT) of the signal [16,17]. The STFT is a linear transform and will not produce any interferences for a multicomponent signal; consequently, the WVD computed based on the STFT will not contain any interferences.…”

Section: Complex-lag Pwvdmentioning

confidence: 99%

Fourth-Order Complex-Lag PWVD for Multicomponent Signals with Application in ISAR Imaging of Maneuvering Targets

Wang

Jiang

2010

Circuits Syst Signal Process

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The fourth-order complex-lag polynomial Wigner-Ville distribution (PWVD) is extended to generate a high resolution time-frequency distribution for multicomponent signals in this paper. For signals with polynomial phase up to order four, the interferences between different components are reduced by the convolution in the frequency domain of the complex-lag PWVD. The complex-lag PWVD can achieve optimal energy concentration, and it is used in the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, where high quality instantaneous ISAR images are obtained. Simulated results demonstrate the effectiveness of the method.

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Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter

Cited by 98 publications

References 18 publications

Two helicopter classification methods with a high pulse repetition frequency radar

Two helicopter classification methods with a high pulse repetition frequency radar

An iterative approach for decomposition of multi-component non-stationary signals based on eigenvalue decomposition of the Hankel matrix

Fourth-Order Complex-Lag PWVD for Multicomponent Signals with Application in ISAR Imaging of Maneuvering Targets

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