Paper Info
Title
Meta Network For Radar Hrrp Noncooperative Target Recognition With Missing Aspects
Abstract
We propose a meta network (MNet) for the problem of target-aspect missing in radar high-resolution range profile (HRRP)-based noncooperative target recognition, where a classifier must be generalized to new aspects not seen in the training set, given only a small number of HRRP data of each new aspect. The MNet is a time domain convolutional neural network (TCNN) that is built based upon recent progress in meta-learning. In effect, it learns a model that is easy and fast to fine-tune, allowing the adaptation to happen in the right space for fast learning. Besides, we construct a new controllable HRRP dataset suitable for the scenario of noncooperative target-aspect missing using electromagnetic simulation. Compared with the traditional methods, the MNet is more efficient and could achieve better performance. Extensive experiments on the simulated HRRP dataset are conducted to illustrate the effectiveness of the proposed method.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9323129
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
high-resolution range profile (HRRP), noncooperative recognition with target-aspect missing (N-CRTAM), meta network (MNet)
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Long Tian100.34
Bo Chen2172.00
Yang Peng300.34
Chuan Du402.70
Zhenhua Wu500.34
Hongwei Liu641666.06