Automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform

Zeng, Deguo; Zeng, Xiangfeng; Cheng, Huang; Tang, Bin

doi:10.1049/iet-rsn.2011.0338

Cited by 48 publications

(29 citation statements)

References 22 publications

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“…To realize the IFR estimation by (17), some parameters should be considered according to the signal processing capability and they are adjustable for the algorithm designer. These parameters are:…”

Section: Ifr Estimation Using a Sliding Windowmentioning

confidence: 99%

“…Four basic IMOPs including the CF, LFM, BPC, and BFC modulations are considered here as examples. The modulation features are extracted from the instantaneous phase and instantaneous frequency law (IFL) curve which is calculated according to (14) and (17).…”

Section: Accurate Modulation Classification Within Modulation Familiesmentioning

confidence: 99%

“…An supervised classification system achieving overall correct classification rate of 98 % at signal-to-noise ratio (SNR) of 6 dB based on multilayer perception networks was proposed in [14] and eight classes of radar signals are classified. Algorithms based on some time-frequency distributions such as the ambiguity function (AF) [15], Zhao Altas and Marks (ZAM) representations [16], the Rihacek distribution and the Hough transform [17], were also applied to extract modulation features for five kinds of radar pulses. These existing algorithms work effectively when the radar IMOPs are in the list of their databases, but fail when they are faced with some complicated or unknown modulated pulses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous radar pulse modulation classification using modulation components analysis

Wang

Qiu

Zhu

et al. 2016

EURASIP J. Adv. Signal Process.

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An autonomous method for recognizing radar pulse modulations based on modulation components analysis is introduced in this paper. Unlike the conventional automatic modulation classification methods which extract modulation features based on a list of known patterns, this proposed method classifies modulations by the existence of basic modulation components including continuous frequency modulations, discrete frequency codes and discrete phase codes in an autonomous way. A feasible way to realize this method is using the features of abrupt changes in the instantaneous frequency rate curve which derived by the short-term general representation of phase derivative. This method is suitable not only for the basic radar modulations but also for complicated and hybrid modulations. The theoretical result and two experiments demonstrate the effectiveness of the proposed method.

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Section: Ifr Estimation Using a Sliding Windowmentioning

confidence: 99%

Section: Accurate Modulation Classification Within Modulation Familiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous radar pulse modulation classification using modulation components analysis

Wang

Qiu

Zhu

et al. 2016

EURASIP J. Adv. Signal Process.

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“…Thereinto, as a significant means to FB, the time-frequency analysis has been developed because it allows description of the instantaneous characteristics of a signal in the two-dimensional (2D) timefrequency space [6][7][8][9][10][11][12][13][14][15]. The authors in [9] proposed a robust method for radar emitter recognition based on the Wigner-Ville distribution (WVD) and transfer learning, the average recognition rate (ARR) reaches more than 90% when signal-to-noise ratio (SNR) is 10 dB.…”

Section: Introductionmentioning

confidence: 99%

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Liu

Shan

2017

EURASIP J. Adv. Signal Process.

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Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

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“…A proper signal classification makes signal demodulation or emitter identification possible. In [1] and [2] authors present methods of the automatic modulation classification (AMC) for an electronic intelligence (ELINT) purposes. One of the possible application of a radar signal waveform is modified nonlinear frequency modulation (NLFM) presented in [3] or waveforms used for synthetic aperture radar (SAR) techniques discussed in [4,5].…”

Section: Introductionmentioning

confidence: 99%

Type of modulation identification using Wavelet Transform and Neural Network

Walenczykowska¹,

Kawalec²

2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Automatic recognition of the signal modulation type turned out to be useful in many areas, including electronic warfare or surveillance. The wavelet transform is an effective way to extract signal features for identification purposes. In this paper there are M-ary ASK, M-ary PSK, M-ary FSK, M-ary QAM, OOK and MSK signals analysed. The mean value, variance and central moments up to five of continuous wavelet transform (CWT) are used as signal features. The principal component analysis (PCA) is applied to reduce a number of features. A multi-layer neural network trained with backpropagation learning algorithm is considered as a classifier. There are two research variants: interclass and intraclass recognition with a wide range of signal-to-noise ratio (SNR).

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Automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform

Cited by 48 publications

References 22 publications

Autonomous radar pulse modulation classification using modulation components analysis

Autonomous radar pulse modulation classification using modulation components analysis

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Type of modulation identification using Wavelet Transform and Neural Network

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