Micro-Doppler-Based Space Target Recognition with a One-Dimensional Parallel Network

Han, Lixun; Feng, Cunqian

doi:10.1155/2020/8013802

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…Four classic signal channel CNNs are selected for comparison: Alexnet, VGG-19, Googlenet, and Resnet-34. We select ballistic target recognition methods that include one-dimensional parallel network (1D-PNet) [33] and dual-channel residual neural network (DCRNN) [40]. At the same time, we select three kinds of multi-view fusion methods that include the multi-view harmonized bilinear network (MHBN) [59], the multi-view convolutional neural networks (MVCNN) [60], multimodal transfer module (MMTM) [61], cross-modal fusion network based on self-attention and residual structure (CFN-SR) [62] and the method in [44] that is based on attention and DGCCA.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To solve the problem, Wang et al combined the advantages of CNN and recurrent neural network (RNN), which can simultaneously extract range-Doppler features and time series features of gesture motion repressively [32]. Authors in [33] used long short-term memory (LSTM) to extract time sequential features of HRRP sequences. Han et al used a onedimensional convolutional neural network to extract the features of the frequency, and then used LSTM to extract the time series features among frequencies, which achieves better results compared to other classic CNNs [34].…”

Section: A Micro-motion Feature Extraction Based On Networkmentioning

confidence: 99%

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Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

2022

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Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: A Micro-motion Feature Extraction Based On Networkmentioning

confidence: 99%

Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

2022

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“…The unit vector in the radial direction from the sonar to the vibrating center O 2 of the target highlight takes the same form as Eq. (7).…”

Section: Motion Features Model For Underwater Targets With Multiple H...mentioning

confidence: 99%

“…In addition to civilian applications, relevant research has also been conducted in the military domain. For instance, based on the estimation of target micro-motion parameters, the identification of warheads has been accomplished, taking into account the different forms of target micro-motions (Gao et al, 2010;Han, Feng, 2020;Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms

Sun,

Zhou,

Peng

2024

Archives of Acoustics

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Motion characterization, including Doppler and micro-Doppler, is crucial for the detection and identification of high-speed underwater targets. Under high-frequency and short-range conditions, underwater targets cannot be simply regarded as single highlight targets as they exhibit a complex structure with multiple scattering centers accompanied by distinct micro-motions. To address this multi-highlight and multi-micro-motion scenario, a model is proposed to characterize the motion features of underwater targets. Firstly, a mathematical model is established to represent the micro-Doppler features based on the single-highlight model. Subsequently, considering the overlap of multiple highlight echoes caused by the high-speed translation of the target and the long pulse detection signal, precise representation is achieved by setting motion positions and calculating time delays within the model. The results represent the echoes of moving targets with multiple highlights and micromotions. Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. This research provides a theoretical foundation for the modeling of underwater moving targets.

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“…9. Other works on micro-Doppler-based recognition of ballistic targets include [101][102][103][104][105][106][107].…”

Section: Space Targets Recognitionmentioning

confidence: 99%

Micro-Doppler Based Target Recognition With Radars: A Review

et al. 2022

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With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has

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Micro-Doppler-Based Space Target Recognition with a One-Dimensional Parallel Network

Cited by 14 publications

References 27 publications

Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms

Micro-Doppler Based Target Recognition With Radars: A Review

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