Three-dimensional precession feature extraction of space targets

Luo, Ying; Zhang, Qun; Yuan, Naichang; Zhu, Feng; Gu, Fengshou

doi:10.1109/taes.2014.110545

Cited by 71 publications

(24 citation statements)

References 17 publications

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“…Reference [1] points out that, in the wideband radar system, the micromotion feature of target can be described by range-slow-time image, where the peak of range profile appears to be a range-slow-time curve (range trajectory) which is determined by the scatterer range ( ). And the range trajectory reflects the micromotion feature of target scatterer.…”

Section: Micromotion Feature Of Conical Target With Precessionmentioning

confidence: 99%

“…The monitoring and recognition of space targets have become more and more important since these space targets such as fragments of satellites, rocket bodies, and other space debris are hazardous to aerospace activities with the increasing outer space explorations [1]. Generally, a space target has complex micromotion such as spinning, precession, and rolling, in addition to the body translation [2].…”

Section: Introductionmentioning

confidence: 99%

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Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Chen¹,

Zhang²,

Luo³

et al. 2017

Journal of Sensors

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The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method.

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Section: Micromotion Feature Of Conical Target With Precessionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Chen¹,

Zhang²,

Luo³

et al. 2017

Journal of Sensors

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“…To solve this problem, many studies have been carried out on parameter estimation based on two trajectories: the high-reolution range profile (HRRP) trajectory in [9][10][11][12] and the MD frequency trajectory in [13,14]. Parameter estimation can be achieved using HRRP trajectories for the multiple scatterers of a ballistic warhead with micromotion.…”

Section: Introductionmentioning

confidence: 99%

Novel Parameter Estimation Method for a Ballistic Warhead with Micromotion

Choi

Jung

Kim

et al. 2020

J Electromagn Eng Sci

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This paper proposes a novel method to estimate the parameters of a ballistic warhead with micromotion. First, the independent component analysis decomposes the echo signals received from the ballistic warhead into individual signals corresponding to each scatterer. Second, the one-dimensional micro-Doppler frequency trajectories are extracted from two-dimensional joint time-frequency images of decomposed individual signals. Finally, the adaptive particle swarm optimization is used to estimate the parameters that best match the mathematical model composed of unknown parameters to the one-dimensional micro-Doppler frequency trajectories. In simulations using a conical warhead model, the parameters for a ballistic warhead with micromotion are accurately estimated.

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“…Sun et al proposed a time-varying interferometric 3D imaging method for space rotating targets based on stepped-frequency chirp signal [14]. In addition, the parametric methods for forming 3D image of micromotion target mainly include 3D imaging of spinning targets based on the fixed scattering center model and imaging of smooth precession cones based on the sliding scattering center model [15][16][17][18]. However, the scattering center trajectory association is not easy to accomplish due to the shadowing effect, limited range resolution, and small angular extent; the interferometric 3D imaging method needs a more complex hardware structure; and the parametric methods have to face a large amount of unknown parameters in imaging the artificial metallic target [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Three-Dimensional Images Based on Estimation of Spinning Target Parameters in Radar Network

et al. 2018

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A high-resolution three-dimensional (3D) image reconstruction method for a spinning target is proposed in this paper and the anisotropy is overcome by fusing different observation information acquired from the radar network. The proposed method will reconstruct the 3D scattering distribution, and the mapping of the reconstructed 3D image onto the imaging plane is identical to the two-dimensional (2D) imaging result. At first, the range compression and inverse radon transform is employed to produce the 2D image of the spinning target. In addition, the process of mapping the spinning target onto the imaging plane is analyzed and the mapping formulas which are to map the point onto the 2D image plane are derived. After the micro-Doppler signature about which every reconstructed point in 2D imaging result is extracted by the Radon transform, the extended Hough transform is adopted to calculate an important parameter about the micro-Doppler signature, and the 3D image reconstruction model for the spinning target is constructed based on the radar network. Finally, the algorithm for solving the reconstruction model is proposed and the 3D image of the spinning target is obtained. Some simulation results are given to illustrate the effectiveness of the proposed method, and results show that the mean square error (MSE) relatively holds a steady trend when the signal-to-noise ratio (SNR) is higher than −10 dB and the MSE of the reconstructed 3D target image is less than 0.15 when SNR is at the level of −10 dB.

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Three-dimensional precession feature extraction of space targets

Cited by 71 publications

References 17 publications

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Novel Parameter Estimation Method for a Ballistic Warhead with Micromotion

Reconstruction of Three-Dimensional Images Based on Estimation of Spinning Target Parameters in Radar Network

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