Effective Discrimination between Warhead and Decoy in Mid-Course
                        Phase of Ballistic Missile

Choi, In-Oh; Jung, Jin‐Myung; Kim, Kyung‐Tae; Park, Sang-Hong

doi:10.5515/kjkiees.2020.31.5.468

Cited by 2 publications

(1 citation statement)

References 12 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network

Feng

Han

2022

Remote Sensing

View full text Add to dashboard Cite

Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.

show abstract

Effective Discrimination between Warhead and Decoy in Mid-Course Phase of Ballistic Missile

Cited by 2 publications

References 12 publications

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

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

Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network

Contact Info

Product

Resources

About