Name
Abstract | ||
|---|---|---|
In this paper, we integrate the advantages of convolutional neural network (CNN) and bidirectional recurrent neural network (BiRNN) with attention mechanism, and propose a CNN-BiRNN based method to recognize the individual high resolution range profile (HRRP). In the proposed method, the CNN is utilized to explore the spatial correlation of raw HRRP data and extract expressive features followed by a BiRNN taking the full consideration of temporal dependence between range cells. Furthermore, in order to enhance the robustness to misalignment, an attentional mechanism is employed after BiRNN to allow the CNN-BiRNN model to focus on the discriminative target area. The combination of CNN and BiRNN with attention mechanism makes the extracted features are not only efficient, but also strongly resistant to the time-shift sensitivity. Experimental results on measured HRRP data demonstrate the effectiveness and the robustness to misalignment of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2969450 | IEEE ACCESS |
Keywords | DocType | Volume |
Radar automatic target recognition (RATR), high-resolution range profile (HRRP), convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention mechanism | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinwei Wan | 1 | 5 | 1.46 |
| Bo Chen | 2 | 304 | 34.22 |
| Yingqi Liu | 3 | 1 | 0.36 |
| Yijun Yuan | 4 | 1 | 0.70 |
| Hongwei Liu | 5 | 416 | 66.06 |
| Lin Jin | 6 | 5 | 1.12 |