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Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition
- Source :
- Signal Processing. 155:268-280
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.
- Subjects :
- business.industry
Computer science
020206 networking & telecommunications
Sample (statistics)
Pattern recognition
02 engineering and technology
law.invention
Range (mathematics)
Dimension (vector space)
Discriminative model
Control and Systems Engineering
law
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
Spectrogram
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
Sensitivity (control systems)
Time domain
Electrical and Electronic Engineering
Radar
business
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 155
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........9a949119101531a410ac8239be674a44