Open Access

ARTICLE

An Aerial Target Recognition Algorithm Based on Self-Attention and LSTM

by Futai Liang^1,2, Xin Chen^1,*, Song He¹, Zihao Song¹, Hao Lu³

1 Department of Intelligence, Early Warning Academy, Wuhan, 430019, China
2 31121 PLA Troops, Nanjing, 210000, China
3 Information Technology Room, Early Warning Academy, Wuhan, 430019, China

* Corresponding Author: Xin Chen. Email:

(This article belongs to the Special Issue: Artificial Neural Networks and its Applications)

Computers, Materials & Continua 2024, 81(1), 1101-1121. https://doi.org/10.32604/cmc.2024.055326

Received 23 June 2024; Accepted 03 September 2024; Issue published 15 October 2024

Abstract

In the application of aerial target recognition, on the one hand, the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise. On the other hand, it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples. Aiming at these problems, an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network (LSTM) is proposed. LSTM can effectively extract temporal dependencies. The attention mechanism calculates the weight of each input element and applies the weight to the hidden state of the LSTM, thereby adjusting the LSTM’s attention to the input. This combination retains the learning ability of LSTM and introduces the advantages of the attention mechanism, making the model have stronger feature extraction ability and adaptability when processing sequence data. In addition, based on the prior information of the multi-dimensional characteristics of the target, the three-point estimation method is adopted to simulate an aerial target recognition dataset to train the recognition model. The experimental results show that the proposed algorithm achieves more than 91% recognition accuracy, lower false alarm rate and higher robustness compared with the multi-attribute decision-making (MADM) based on fuzzy numbers.

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Keywords

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