Target identification using bistatic high‐resolution range profiles

Lee, Seung-Jae; Jeong, Seong‐Jae; Yang, Eunjung; Kim, Kyung‐Tae

doi:10.1049/iet-rsn.2016.0316

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…For a BMD system to intercept the space targets in this phase, it is important to distinguish the space targets from debris and decoys. For this purpose, the HRRP and inverse-synthetic aperture radar (ISAR) images are used [4,5], but it is difficult to expect good performance due to the similar shape of decoys and space targets. Unlike conventional radar signatures such as HRRP and ISAR images, research on identifying targets using micro-Doppler signatures has been extensively studied for decades.…”

Section: Introductionmentioning

confidence: 99%

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

Lee

Kim

Min

et al. 2022

Sensors

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Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram.

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Section: Introductionmentioning

confidence: 99%

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

Lee

Kim

Min

et al. 2022

Sensors

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show abstract

“…Therefore, it is necessary to discriminate the warhead from the decoy. However, conventional methods based on the range profile and the inverse synthetic aperture image in [1] and [2] have limited ability to discriminate them because of the similarity in shape and the overall trajectory of the warhead and the decoy. The problems of existing methods can be overcome by exploitingthe difference in their micro-Doppler (MD) effect [3][4][5][6][7][8]; the warhead is much heavier due to various devices, and thus its micromotion is dif-ferent from that of the decoy.…”

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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“…Meanwhile, Guo et al utilized one-dimensional residual-inception network to recognize HRRPs [11]. To improve the ability of recognizing HRRPs with bistatic radar, RELAX method and novel feature extraction processing have been used [12]. Nevertheless, the difficulties in recognizing HRRPs under the condition of low signal-to-noise ratio (SNR) have not been solved effectively.…”

Section: Introductionmentioning

confidence: 99%

A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

Dai

Wang

2019

Sensors

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High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

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Target identification using bistatic high‐resolution range profiles

Cited by 11 publications

References 16 publications

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

Novel Parameter Estimation Method for a Ballistic Warhead with Micromotion

A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

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