A time-frequency concentration criterion using grayscale erosion

Wang, Guanghui; Wang, Hao-chen; Zhu, Mingzhe

doi:10.1109/siprocess.2016.7888292

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Another critical parameter for SPWVD is the window length. Actually, there have been some related works [31][32][33] on parameters selection of time-frequency distribution. Inspired by [31], we define three levels of window lengths: small, medium and large, and choose "33, 133, 233" as the concrete representations of them, respectively.…”

Section: Smooth Pseudo-wigner-ville Distributionmentioning

confidence: 99%

Radar signal recognition based on triplet convolutional neural network

Liu

2021

EURASIP J. Adv. Signal Process.

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Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

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Section: Smooth Pseudo-wigner-ville Distributionmentioning

confidence: 99%

Radar signal recognition based on triplet convolutional neural network

Liu

2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

A time-frequency concentration criterion using grayscale erosion

Cited by 1 publication

References 7 publications

Radar signal recognition based on triplet convolutional neural network

Radar signal recognition based on triplet convolutional neural network

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