Multi-Function Radar Work Mode Recognition Based on Encoder-Decoder Model

Chen, Hongyu; Feng, Kangan; Kong, Yukai; Zhang, Youmin; Yu, Xianxiang; Yi, Wei

doi:10.1109/igarss46834.2022.9884556

Cited by 5 publications

(3 citation statements)

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“…This section compares the effectiveness of the CNN, the HSSLSTM [19], the GRU [31], and the proposed DP-ATCN-based MFR working mode recognition algorithm. The test accuracy of the four comparison networks and the proposed network is shown in Table 4.…”

Section: Comparison Of the Proposed Methods With Other Algorithmsmentioning

confidence: 99%

“…Recurrent neural networks (RNNs) are specifically designed for processing word sequences and have achieved significant success in related applications [26,27]. In recent years, recurrent neural networks (RNN) and their variants [28][29][30][31] have received extensive attention in the field of MFR working mode recognition. By using two vital RNN variants, gated recurrent unit (GRU) and Long Short-Term Memory (LSTM), [30] fully exploited the ability of GRU to automatically learn the characteristics of radar signal sequences.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, [19] proposed a novel hierarchical sequence-tosequence (seq2seq) long short-term memory network for automatic MFR work mode recognition. Moreover, [11,31] designed an Encoder-Decoder model based on GRU to reconstruct the temporal characteristics of the complex pulse group sequence. In [32], MFR working mode recognition was addressed by utilizing hierarchical mining and analyzing high-dimensional patterns of PDWs from different modes.…”

Section: Related Workmentioning

confidence: 99%

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Shipborne Multi-Function Radar Working Mode Recognition Based on DP-ATCN

Tian

Zhang

et al. 2023

Remote Sensing

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There has been increased interest in recognizing the dynamic and flexible changes in shipborne multi-function radar (MFR) working modes. The working modes determine the distribution of pulse descriptor words (PDWs). However, building the mapping relationship from PDWs to working modes in reconnaissance systems presents many challenges, such as the duration of the working modes not being fixed, incomplete temporal features in short PDW slices, and delayed feedback of the reconnaissance information in long PDW slices. This paper recommends an MFR working mode recognition method based on the ShakeDrop regularization dual-path attention temporal convolutional network (DP-ATCN) with prolonged temporal feature preservation. The method uses a temporal feature extraction network with the Convolutional Block Attention Module (CBAM) and ShakeDrop regularization to acquire a high-dimensional space mapping of temporal features of the PDWs in a short time slice. Additionally, with prolonged PDW accumulation, an enhanced TCN is introduced to attain the temporal variation of long-term dependence. This way, secondary correction of MFR working mode recognition results is achieved with both promptness and accuracy. Experimental results and analysis confirm that, despite the presence of missing and spurious pulses, the recommended method performs effectively and consistently in shipborne MFR working mode recognition tasks.

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Section: Comparison Of the Proposed Methods With Other Algorithmsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Shipborne Multi-Function Radar Working Mode Recognition Based on DP-ATCN

Tian

Zhang

et al. 2023

Remote Sensing

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Pulse‐level work state recognition of multifunction radar based on MC‐RSG

Qin,

Ren,

Yang

et al. 2024

IET Radar Sonar & Navi

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Accurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC‐RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions.

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