Separation of Dynamic RCS using Hough Transform in Multi-target Environment

Kim, Yujin; Choi, Young-Jae; Choi, In-Sik

doi:10.14801/jkiit.2019.17.9.91

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Efficient ballistic target recognition is a key issue for ballistic missile defense systems, especially the fast and effective recognition of missile warheads from numerous decoy targets is a very challenging task. For ballistic target recognition, the infrared radiation [1][2][3][4][5], micro-Doppler [6][7][8] radar crosssection [9][10][11][12], and high-resolution range profile (HRRP) [13][14][15][16] of ballistic targets are all considered. Among them, HRRP represents the radial distribution of target scattering centers on radar line of sight and benefits from its rich structural information of the target and the easiness of obtaining, which has become an effective feature for radar automatic target recognition (RATR) [13,17].…”

Section: Introductionmentioning

confidence: 99%

Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers

Xiang,

Wang,

Lai

et al. 2023

Int J Comput Intell Syst

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Ballistic missile defense systems require accurate target recognition technology. Effective feature extraction is crucial for this purpose. The deep convolutional neural network (CNN) has proven to be an effective method for recognizing high-resolution range profiles (HRRPs) of ballistic targets. It excels in perceiving local features and extracting robust features. However, the standard CNN's fully connected manner results in high computational complexity, which is unsuitable for deployment in real-time missile defense systems with stringent performance requirements. To address the issue of computational complexity in HRRP recognition based on the standard one-dimensional CNN (1DCNN), we propose a lightweight network called group-fusion 1DCNN with layer-wise auxiliary classifiers (GFAC-1DCNN). GFAC-1DCNN employs group convolution (G-Conv) instead of standard convolution to effectively reduce model complexity. Simply using G-Conv, however, may decrease model recognition accuracy due to the lack of information flow between feature maps generated by each G-Conv. To overcome this limitation, we introduce a linear fusion layer to combine the output features of G-Convs, thereby improving recognition accuracy. Additionally, besides the main classifier at the deepest layer, we construct layer-wise auxiliary classifiers for different hierarchical features. The results from all classifiers are then fused for comprehensive target recognition. Extensive experiments demonstrate that GFAC-1DCNN with such simple and effective techniques achieves higher overall testing accuracy than state-of-the-art ballistic target HRRP recognition models, while significantly reducing model complexity. It also exhibits a higher recall rate for warhead recognition compared to other methods. Based on these compelling results, we believe this work is valuable in reducing workload and enhancing missile interception rates in missile defense systems.

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