Radar HRRP target recognition based on the multiplicative RNN model

被引：0
|
作者：
Xu B. [1 ]
Zhang Y. [1 ]
Zhang Q. [1 ]
Wang F. [2 ]
Zheng G. [1 ]
机构：
[1] Air and Missile Defence college, Air Force Engineering University, Xi'an
[2] School of Communication and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2021年 / 48卷 / 02期
关键词：
High resolution range profile; Multiplicative recurrent neural network; Radar automatic target recognition; Target-aspect sensitivity; Temporal correlation;
D O I：
10.19665/j.issn1001-2400.2021.02.007
中图分类号：
学科分类号：
摘要：
The traditional HRRP recognition methods do not consider the temporal correlation, and the azimuth sensitivity of HRRP results in the temporal variation of the samples.This paper proposes a multiplicative recurrent neural network.In this paper, HRRP samples are converted into the sequence form first, which is used to consider the correlation between range cells.In order to alleviate the mismatch between the HRRP sequence variation caused by azimuth sensitivity and the model with fixed parameters, the model adaptively selects the corresponding parameters according to the input data, and extracts robust features from the HRRP sequence.Finally, the voting strategy is adopted to fuse the information at all time steps and predict the sample categories.Experimental results with measured data show that the current model can effectively extract discriminative features and identify targets. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：49 / 54
页数：5
相关论文
共 13 条
  • [1] GUO Z H, LI S H., One-dimensional Frequency-domain Features for Aircraft Recognition from Radar Range Profiles, IEEE Transactions on Aerospace and Electronic Systems, 46, 4, pp. 1880-1892, (2010)
  • [2] LI Zhipeng, MA Tianxiang, DU Lan, Et al., Multi-class Classifier Design for Feature Fusion in Radar HRRP Recognition, Journal of Xidian University, 40, 1, pp. 111-117, (2013)
  • [3] XU Danlei, DU Lan, WANG Penghui, Et al., Radar HRRP Target Recognition by Utilizing Multitask Sparse Learning with A Small Training Datasize, Journal of Xidian University, 43, 2, pp. 23-28, (2016)
  • [4] FENG B, CHEN B, LIU H., Radar HRRPTarget Recognition with Deep Networks, Pattern Recognition, 61, pp. 379-393, (2017)
  • [5] DU L, WANG P, LIU H, Et al., Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition, IEEE Transactions on Signal Processing, 59, 7, pp. 3182-3196, (2011)
  • [6] XU B, CHEN B, WAN JW, Et al., Target-aware Recurrent Attentional Network for Radar HRRP Target Recognition, Signal Processing, 155, 2, pp. 268-280, (2019)
  • [7] XU Bin, CHEN Bo, LIU Jiaqi, Et al., Radar HRRP Target Recognition by the Bidirectional LSTM model, Journal of Xidian University, 46, 2, pp. 29-34, (2019)
  • [8] LI Long, LIU Zheng, Noise-robust Multi-feature Joint Learning HRRP Recognition Method, Journal of Xidian University, 45, 4, pp. 57-62, (2018)
  • [9] DU C, CHEN B, XU B, Et al., Factorized Discriminative Conditional Variational Auto-encoder for Radar HRRP Target Recognition, Signal Processing, 158, 5, pp. 176-189, (2019)
  • [10] WAN J W, CHEN B, XU B, Et al., Convolutional Neural Networks for Radar HRRP Target Recognition and Rejection, Eurasip Journal on Advances in Signal Processing, 2019, 1, (2019)
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