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23 May 2023 Classification of ground-moving targets based on LSTM
Yanyan Ma
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Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264505 (2023) https://doi.org/10.1117/12.2680803
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
Tracked vehicles and wheeled vehicles are classical weapons on the land battlefield, so their robust classification of them is an important part of radar automatic target recognition due to correct classification being of great significance to making decisions on the battlefield. To reduce the dependence of feature extraction on manual work, a novel classification method based on a Long Short-Term Memory network (LSTM) with amazing advantages in processing sequential data is proposed. The proposed method could mine the correlation inside the target, and automatically extract robust features from the target. The classification performance shows the validity of the proposed method with accuracy, precision, recall, and F1-score based on measured data respectively reaching 98.55%, 97.26%, 97.77% and 97.43% under the condition that the LSTM model is designed by four layers with the number of neurons in each hidden layer is 32. The experimental results by manually adding Gaussian white noise of different intensities to the test dataset verify the noise robustness of the proposed method. The compared results with other work indicate that the proposed method has better performance.
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Yanyan Ma "Classification of ground-moving targets based on LSTM", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264505 (23 May 2023); https://doi.org/10.1117/12.2680803
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KEYWORDS
Feature extraction
Signal to noise ratio
Neurons
Education and training
Radar
Data modeling
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