Synchrosqueezing Phase Analysis on Micro-Doppler Parameters for Small UAVs Identification With Multichannel Radar

Zhao, Yichao; Su, Yi

doi:10.1109/lgrs.2019.2924266

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…To align SST coefficients of the TF distribution, the partitioning technique proposed in Ahrabian and Mandic [12] is used to separate the TF plane into 2 k frequency bands with equal width, depicted as [20]:

w_{k, l} = [\frac{l}{2^{k + 1}}, \frac{l + 1}{2^{k + 1}}]

where k = 0,1,L, K is the level of frequency bands, and l = 0,1,L,2 k −1 denotes the index for the k frequency band.…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

“…In this case, M p , i is defined as the i th IMF of the p th channel for selecting IMFs with rotation. The phase spectrum is acquired as [2, 20]:

\begin{array}{l} F {boldM}_{p, i} = PF (M_{p, i}) \\ = F (\ln (M_{p, i})) + F (P R) \end{array}

where PF (•) represents the phase Fourier transform and F (•) is the Fourier transform. The principle of phase Fourier transform is equivalent to the theory of the cyclic autocorrelation functions of the phase term in Zhao et al.…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

“…The CWT‐based SST (CSST) technique, given by a wavelet transform, could enhance the TF representation by reassigning coefficients using an estimate of IF. However, the m‐D signal not composed of purely harmonic modes could not be analysed effectively by these techniques [20]. Because of the mode‐mixing problem among R‐IMFs, the TF distributions contain multiple TF ridges, which are infeasible for estimating rotation parameters.…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

“…E p ( k , l ) indicates the energy of joint spectrum [12]. Then, the joint bandwidth is obtained as [20]:

B (k, l) = \sqrt{\frac{1}{2 π} \int_{0}^{\infty} {(w - \bar{w})}^{2} S_{k, l} (w) d w}

where

\bar{w} = \frac{1}{2 π} \int_{0}^{\infty} w S_{k, l} (w) d w

represents the joint mean phase frequency [20].…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

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Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Zhao

2021

IET Microwaves Antenna & Prop

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The micro-Doppler (m-D) effect depends on the rotation of rotor blades in addition to the translation of the platform. Thus it is a characteristic for identifying small unmanned aerial vehicles (UAVs). However, compared with the Doppler signal induced by the translation of the platform, the m-D signal is weak. In this article, a highly localised dataassociation method, intrinsic synchrosqueezing analysis (ISA), is proposed for estimating m-D characteristics from the returned signal of small UAVs with a dual-channel radar. Employing synchrosqueezing transform on intrinsic mode functions derived from noiseassisted multivariate empirical mode decomposition, the proposed ISA method separates the Doppler signal and enables denoising and sharpening time-frequency representation of the m-D signal. Simulation results confirm the theoretical analysis, showing the feasibility of estimating m-D features in a noisy environment. Applications on field data illustrate brighter prospects for identifying small UAVs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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w_{k, l} = [\frac{l}{2^{k + 1}}, \frac{l + 1}{2^{k + 1}}]

where k = 0,1,L, K is the level of frequency bands, and l = 0,1,L,2 k −1 denotes the index for the k frequency band.…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

“…In this case, M p , i is defined as the i th IMF of the p th channel for selecting IMFs with rotation. The phase spectrum is acquired as [2, 20]:

\begin{array}{l} F {boldM}_{p, i} = PF (M_{p, i}) \\ = F (\ln (M_{p, i})) + F (P R) \end{array}

Section: Proposed Isa Methodsmentioning

confidence: 99%

Section: Proposed Isa Methodsmentioning

confidence: 99%

“…E p ( k , l ) indicates the energy of joint spectrum [12]. Then, the joint bandwidth is obtained as [20]:

B (k, l) = \sqrt{\frac{1}{2 π} \int_{0}^{\infty} {(w - \bar{w})}^{2} S_{k, l} (w) d w}

where

\bar{w} = \frac{1}{2 π} \int_{0}^{\infty} w S_{k, l} (w) d w

represents the joint mean phase frequency [20].…”

Section: Proposed Isa Methodsmentioning

confidence: 99%

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Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Zhao

2021

IET Microwaves Antenna & Prop

Self Cite

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show abstract

“…For example, the fractional Fourier method (FrFT) was used to estimate the micro-Doppler frequencies of ship and human targets [9,10]. The synchronous compressed sensing method (SST) enhanced the positioning characteristics of the time-frequency method by redistributing the energy of the time-frequency coefficients and was used in space target imaging and propeller Doppler estimation [11,12]. Although these subsequent algorithms improve the performance of traditional time-frequency methods, they remain sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Micro‐Doppler frequency estimation and association via the inverse‐free complex SBL TVAR method

Dai

Liu

et al. 2021

IET Radar Sonar & Navi

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A micro-Doppler frequency estimation and association algorithm of the space target based on the time-varying autoregressive (TVAR) model is proposed herein. The calculation time of the existing algorithms grows significantly with the problem size, and micro-Doppler frequencies of different scattering centres are associated incorrectly where the frequencies intersect. The echo of the space target is modelled by the TVAR model, then the TVAR coefficients and instantaneous frequencies are estimated through the fast sparse Bayes learning (SBL) algorithm, finally, the micro-Doppler frequencies of different scattering centres are associated in the frequency-frequency change rate domain. The effectiveness and rapidity of the proposed algorithm is demonstrated by simulation results.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Aerial Target Recognition With Enhanced Micro-Doppler Dynamic Features Based on Frequency-Modulated Continuous-Wave Radar

Wu,

Liu,

et al. 2023

IEEE Sensors J.

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Synchrosqueezing Phase Analysis on Micro-Doppler Parameters for Small UAVs Identification With Multichannel Radar

Cited by 11 publications

References 15 publications

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Micro‐Doppler frequency estimation and association via the inverse‐free complex SBL TVAR method

Aerial Target Recognition With Enhanced Micro-Doppler Dynamic Features Based on Frequency-Modulated Continuous-Wave Radar

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