Research on radar clutter recognition method based on LSTM

Song, Li; Wang, Shengli; Xie, Dongyue

doi:10.1049/joe.2019.0242

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…When the order of the input sequence changes, the output result will also change. When the time interval between nodes is long, the problem of gradient expansion explosion or gradient disappearance will be caused by multiple multiplication of matrices [21][22][23]. LSTM can solve these problems.…”

Section: Neural Network Of Lstmmentioning

confidence: 99%

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Chen,

Wang,

Chen

2024

J. of Syst. Eng. Electron.

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The rapid development of unmanned aerial vehicle (UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation. In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets. Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM) Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation, while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

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Section: Neural Network Of Lstmmentioning

confidence: 99%

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Chen,

Wang,

Chen

2024

J. of Syst. Eng. Electron.

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“…With the help of the idea of the basic data model in the database system, the main purpose of introducing [22] a hierarchical model structure for Web data is to support different Web data model to enable proxy cache users and administrators to efficiently utilize rich data resources. MEI emphasizes where, focusing on where the movement occurs [23]. It records the accumulated motion energy in the sequence.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Cloud Service Platform for English Video Teaching Based on Detailed Content Recognition Algorithm

2024

International Journal of Science and Engineering Applications

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“…For instance, In 2019, Zhao et al [33] predicting sea clutter power based on LSTM, and achieved lower prediction error than BP NN [16]. Also in 2019, Li et al [34] identify clutter points after target detection based on LSTM and achieved higher recognition accuracy than SVM.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Zhang

et al. 2019

Remote Sensing

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In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

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Research on radar clutter recognition method based on LSTM

Cited by 4 publications

References 4 publications

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Track Correlation Algorithm Based on CNN-LSTM for Swarm Targets

Implementation of Cloud Service Platform for English Video Teaching Based on Detailed Content Recognition Algorithm

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

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