Research on Micro-motion Modeling and Feature Extraction of Passive Bistatic Radar Based on CMMB Signal

Xia, Saiqiang; Jiang, Haiman; Cai, Wanyong; Yang, Jing; Zhang, Chaowei; Chen, Wenfeng

doi:10.1088/1742-6596/2213/1/012013

Cited by 1 publication

(1 citation statement)

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“…Micro-range is generally represented as HRRP sequence and TR map, while micro-Doppler is mainly represented as TF spectrum and cadence velocity diagram (CVD) map [11]. Currently, ballistic target recognition based on micro-range and micro-Doppler features has become a major research direction [12,13].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%