Comparison of unintentional frequency and phase modulation features for specific emitter identification

Ye, Hongxia; Liu, Zhe; Jiang, Wei

doi:10.1049/el.2012.0831

Cited by 27 publications

(32 citation statements)

References 4 publications

(2 reference statements)

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“…Intuitive systemic model [2] is established to effectively describe and explain the fingerprint features. Generally, pulse feature [3], frequency-domain distribution [4,5], and unintentional phase modulation [6] are selected as unintentional representations. However, parameter-level methods require stringent design for model establishment and feature selection.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Zhou¹,

Zhang²,

Zhang³

2019

PIER M

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Aimed at the deficiency of conventional parameter-level methods in radar specific emitter identification (SEI), which heavily rely on empirical experience and cannot adapt to the waveform change, a novel algorithm is proposed to extract specific features and identify in Hilbert-Huang transform domain. Firstly, 2-dimensional physical representation of emitters is formed with Hilbert-Huang transform (HHT). Based on this, 4 types of multi-view features are constructed, and the feature space is spanned by elaborating the extraction. Principal components, between-class similarity, spectrum entropy, and deep architecture are used to describe the subtle features. Finally, support vector machine (SVM) is selected as the classifier to realize identification to alleviate the small sample problem. Experimental results show that the proposed algorithm realizes specific identification using 4 intentional modulations of simulated data. The selected 4 types of unintentional representations are feasible to discriminate identical emitters. Additionally, the proposed algorithm obtains higher accuracy than typical signal-level methods in the signal-to-noise ratio (SNR) range [0, 20] dB.

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Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Zhou¹,

Zhang²,

Zhang³

2019

PIER M

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“…Radar emitter identification plays an important role in electronic support measure (ESM) systems. In general, the identification procedure is composed of first extracting features such as the type of intentional modulation and unintentional modulation features from the received pulses, and then determining the radar emitter from which these pulses should come [1][2][3][4]. Intrapulse feature extraction basically relies on the pulse modulation waveform, since it contains the whole information regarding how the pulse is modulated with time.…”

Section: Introductionmentioning

confidence: 99%

Near‐optimal estimation of radar pulse modulation waveform

Liu

Huang

et al. 2016

IET signal process.

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In this study, the authors focus on pulse modulation waveform estimation based on a sequence of intercepted pulses from the same radar emitter. In general, modulation waveform estimators first perform pulse alignment in time and frequency separately, and then accumulate the aligned pulses to estimate the waveform. However, commonly used alignment methods may lead to considerable alignment errors which are difficult to detect and compensate, especially under low signal-to-noise ratio (SNR) conditions, resulting in non-ideal effects of accumulation. This study proposes a robust and nearly optimal modulation waveform estimation algorithm. The new algorithm first aligns pulses in time and frequency jointly via the cross-ambiguity function to avoid the transfer and accumulation of alignment errors. After that, an iterative maximum-likelihood estimator is invoked to achieve the waveform estimation. Theoretical analysis and extensive experiments show that the proposed algorithm has much smaller alignment errors and better modulation waveform and modulation parameter estimation ability than competing methods at low SNRs, and can approach the ideal case. Moreover, this algorithm does not make any assumption on the type of modulation and is computationally efficient, thus having broad applications.

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“…It can be applied to communication and radar emitters in both civil and military areas. The realisation of SEI relies on unintentional modulation on pulse (UMOP) which is caused by the specific electrical characters of devices in different emitters [1][2][3][4][5][6].…”

mentioning

confidence: 99%

“…Randomly choose three feature vectors as training instances for each emitter and the remaining ones as testing instances. The nearest neighbour classifier [2,5] is used for recognition, and the recognition ratios are averaged over 100 trials. Other parameters are set the same as those of the above experiment.…”

mentioning

confidence: 99%

Frequency‐domain distribution and band‐width of unintentional modulation on pulse

Huang

Liu

et al. 2016

Electron. lett.

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Unintentional modulation on pulse (UMOP) is the basis for specific emitter identification and has been studied for decades. However, the isolated distribution of UMOP in frequency domain has not been investigated. This Letter first gives the signal model of UMOP, and then evaluates its frequency-domain distribution based on sinc-function fitting. Experiments on real-world data validate the effectiveness of the proposed method, and further indicate that UMOP mainly distributes in low frequencies with dramatically limited bandwidth. Furthermore, the proposed distribution density can also serve as a practical UMOP feature which exhibits better recognition performance than conventional spectrum features.

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Comparison of unintentional frequency and phase modulation features for specific emitter identification

Cited by 27 publications

References 4 publications

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Near‐optimal estimation of radar pulse modulation waveform

Frequency‐domain distribution and band‐width of unintentional modulation on pulse

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