A method for extracting micro-motion features of rotor targets based on GS-IRadon algorithm

Long, Ming; Yang, Jun; Xia, Saiqiang; Lv, Mingjiu; Chen, Wenfeng

doi:10.1016/j.dsp.2023.104253

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…In order to achieve feature extraction, the IRadon algorithm is used to transform the sine modulated signal into the parameter space [33]. In Radon transform, the signal can be represented as…”

Section: Time-frequency Domain Characteristics Analysis Of Rotor Targetsmentioning

confidence: 99%

“…Therefore, this paper considers using the flash's features in the echo time-frequency results and uses deep learning networks (FCN, U-Net) to inverse the timefrequency results containing sinusoidal modulation. Then, the inversed results are used to extract micro-motion features by the GS-IRadon algorithm [33] to achieve the rotor target extraction micro-motion feature. Compared to the IRadon transform performed iteratively, the GS-IRadon uses the golden search ratio to search for parameters, which can minimise the times of iterations.…”

Section: Introductionmentioning

confidence: 99%

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Micro‐motion signal time‐frequency results inversion of rotor targets under low signal‐to‐noise ratios

Long,

Yang,

et al. 2024

IET Radar Sonar & Navi

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A signal time‐frequency results inversion method is proposed for extracting micro‐motion features of rotor targets under low signal‐to‐noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro‐motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time‐frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time‐frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time‐frequency results containing sinusoidal modulation based on the flash's features in the time‐frequency results. Based on the inversion time‐frequency results, the GS‐IRadon algorithm is used to extract micro‐motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time‐frequency results under low SNRs, providing a new technical approach for micro‐motion feature extraction. Time‐frequency results inversion is a novelty method used to achieve micro‐motion feature extraction of rotor targets.

show abstract

Section: Time-frequency Domain Characteristics Analysis Of Rotor Targetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Micro‐motion signal time‐frequency results inversion of rotor targets under low signal‐to‐noise ratios

Long,

Yang,

et al. 2024

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

show abstract

A method for extracting micro-motion features of rotor targets based on GS-IRadon algorithm

Cited by 1 publication

References 21 publications

Micro‐motion signal time‐frequency results inversion of rotor targets under low signal‐to‐noise ratios

Micro‐motion signal time‐frequency results inversion of rotor targets under low signal‐to‐noise ratios

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