Function Recognition Of Multi-function Radar Via CNN-GRU Neural Network

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

doi:10.23919/irs54158.2022.9904998

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…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%

“…In [32], MFR working mode recognition was addressed by utilizing hierarchical mining and analyzing high-dimensional patterns of PDWs from different modes. The authors of [11] suggest a CNN-GRU approach that utilizes the 1D-CNN structure to process lengthy input sequences and decrease computation time while employing GRU to acquire intricate sequence features for recognition results. Similarly, [33] adopts a GRU network as an encoder to extract temporal features and transformer decoder layers as decoders to produce prediction results, thereby addressing a signal temporal prediction issue concerning multiple objectives.…”

Section: Related Workmentioning

confidence: 99%

“…This means that when the input is less than 0, the gradient of the ReLU is 0, which cannot update the parameters and may cause the "dying ReLU" problem. To solve the drawbacks, we use Leaky ReLU instead of ReLU, as shown in Equation (11).…”

Section: ( ) ( ) ( )mentioning

confidence: 99%

“…Recognizing the working mode can assist the reconnaissance system in quickly comprehending the target's intentions and directing jamming strategies. However, the MFR's flexible waveform and beam, combined with overlapping parameter distributions, can create tenuous associations between the pulse description words (PDWs) and working modes, posing a serious challenge to the reconnaissance system's perception of radar workings modes [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: ( ) ( ) ( )mentioning

confidence: 99%

Section: Introductionmentioning

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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“…Currently, cognitive electronic reconnaissance has achieved some research results in the classification and processing of radiation source signals. Some researchers have optimised deep learning algorithms at the classifier level to enhance the ability of radiation source signal classification under weak a priori conditions [9][10][11][12]. In terms of unknown signals, Stinco et al derived a new algorithm for channel parameter estimation [13].…”

Section: Introductionmentioning

confidence: 99%

Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

Zhang,

Chen,

Yan

et al. 2023

IET Radar Sonar & Navi

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The integration of electronic reconnaissance platforms and payloads in the electromagnetic and spatial domains of cognition is necessary for the successful execution of cognitive electronic reconnaissance missions. However, in partially observable environments, platforms and payloads interact in separate operational domains, making it a challenge for the model. The authors create a theoretical model of cognitive electronic reconnaissance, present an analysis method of model elements and derives adequate requirements for optimal radiation source sorting or recognition. Then, a 3D cognitive reconnaissance simulator ‘Scouter’ to simulate the dynamic interaction process of space electronic reconnaissance missions is developed and a deep reinforcement learning framework to optimise the electronic reconnaissance strategies of Unmanned Aerial Vehicle (UAV) is designed. For the first time, a method for judging reconnaissance area coverage using a behavioural angle is proposed. This method enables a deep reinforcement learning‐based UAV with better reconnaissance behaviours and to realise the stable targeting of the target by the receiving antenna beam during reconnaissance. Moreover, the fast convergence of UAV agent training is ensured through the normalisation of partially observable status and reward shaping. Verified by adequate numerical simulations, a trained UAV can complete a 100 reconnaissance cycles mission with a completion rate of 91.9%. Even after the reconnaissance cycles are prolonged to 600, the mission completion rate can still be maintained at 78.5%. The most satisfying aspect is that the UAV comprehends the idea of reconnaissance and behaves in a way that is typical of it, such as by reciprocating or circling the edge of the radar's detecting power.

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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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Function Recognition Of Multi-function Radar Via CNN-GRU Neural Network

Cited by 6 publications

References 11 publications

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

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

Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

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

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