Radar Maneuvering Target Motion Parameter Estimation Based on Hough Transform and Polynomial Chirplet Transform

Lin, Hsuan-Hung; Zeng, Chao; Zhang, Hai; Ge, Jian

doi:10.1109/access.2021.3061590

Cited by 7 publications

(14 citation statements)

References 34 publications

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“…where the tuple (u, v) stands for the index of the vth target at the b n RWC u th range sampling cell after RM compensation with the uth pair of parameters (12), A 0 ðu;vÞ is the amplitude of the (u, v)th target's echo signal, and a i,(u,v) , i = 1, 2, 3, is the corresponding motion parameter.…”

Section: Rwc Trajectory Confirmation and Darmentioning

confidence: 99%

“…After the quadrature demodulation and pulse compression, the echo signal in f r − t m domain can be approximated as [12].

\begin{matrix} right S_{normalc} false(t_{m}, f_{r} false) = \sum_{k = 0}^{K - 1} & left {\overset{A}{true}}_{k} rect (\frac{f_{r}}{K_{r} T_{p}}) \\ right & left \times \exp [- j \frac{4 π (f_{r} + f_{normalc}) Δ r_{k} (t_{m})}{c}], \end{matrix}

where f r is the range frequency corresponding to t n , t n is the discrete version of the fast time t ′ = t − mT r − 2 r c / c with the sampling rate f s , and

{\tilde{A}}_{k}

is the amplitude of the k th target's echo signal in f r − t m domain.…”

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

“…After the quadrature demodulation and pulse compression, the echo signal in f r − t m domain can be approximated as [12].…”

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

“…By taking the inverse Fourier transform (IFT) of ( 4), we can obtain the so-called complex range profiles, which can be expressed as [12].…”

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

“…From Figure 4b, the three trajectories, corresponding to the four targets with the motion parameters listed in Table 2, can be accumulated as three significant peaks in ρ − θ space by the HT, as shown in Figure 5. We can robustly identify these peaks via the non-maximum suppression algorithm [17] with appropriate parameters and estimate the corresponding parameters by using (12). For the Target A and B, their trajectories cannot be separated in the standard Hough parameter space by only exploiting the radial range and velocity information about the moving targets.…”

Section: Detection Of the Targets' Trajectories From The Enhanced Ima...mentioning

confidence: 99%

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Motion parameter estimation of high‐speed manoeuvering targets based on hybrid integration and synchrosqueezing transform

Lin

Zeng

Zhang

et al. 2022

IET Radar Sonar & Navi

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In this study, we address the problem of estimating the motion parameters of multiple high‐speed manoeuvering targets under low signal‐to‐noise ratio (SNR) environment with a low‐range resolution radar, where the target movement could cause the echo signal energy seriously spread across both the range and Doppler frequency coordinates, and the returned signals from different targets would merge into each other. Based on the subaperture feature enhancement, Hough transform (HT) and the second‐order short‐time Fourier‐based synchrosqueezing transform (FSST2), a novel method, named as EHFSST2, is proposed. The proposed method can robustly extract the targets of interest from environment and accurately estimate the targets' motion parameters. Thanks to its ability to compensate the effects of the range walk, curvature, and the Doppler ambiguity, spreading, wrapping, and the energy of echo signal can be coherently integrated along the target's individual moving trajectory. In addition, several numerical experiments are provided to demonstrate the effectiveness. The results show that the proposed method is superior to some existing methods in terms of the anti‐noise ability, measurement accuracy and integration performance.

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Section: Rwc Trajectory Confirmation and Darmentioning

confidence: 99%

“…After the quadrature demodulation and pulse compression, the echo signal in f r − t m domain can be approximated as [12].

\begin{matrix} right S_{normalc} false(t_{m}, f_{r} false) = \sum_{k = 0}^{K - 1} & left {\overset{A}{true}}_{k} rect (\frac{f_{r}}{K_{r} T_{p}}) \\ right & left \times \exp [- j \frac{4 π (f_{r} + f_{normalc}) Δ r_{k} (t_{m})}{c}], \end{matrix}

where f r is the range frequency corresponding to t n , t n is the discrete version of the fast time t ′ = t − mT r − 2 r c / c with the sampling rate f s , and