Battlefield Situation Awareness Model Using Convolutional LSTM

被引：0

作者：

Kim, Pil-Song ^{[1
]}

Hyun, Sun-young ^{[1
]}

Ha, Young-guk ^{[2
]}

机构：

[1] Konkuk Univ, Dept Artificial Intelligence, Seoul, South Korea

[2] Konkuk Univ, Dept Comp Sci & Engn, Seoul, South Korea

来源：

2023 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING, BIGCOMP | 2023年

关键词：

Machine Learning; Battlefield Situation Awareness; LSTM; CNN;

D O I：

10.1109/BigComp57234.2023.00086

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper, a machine learning model for battlefield situation awareness is implemented using a model that utilizes the characteristics of both neural network models. The proposed neural network model is based on a Convolutional Neural Network(CNN)[1] model that extracts spatial feature and a Long Short-Term Memory(LSTM)[2] model, a type of Recurrent Neural Network(RNN) used to predict time series data. By using these two together, both temporal and spatial features of the data are learned to predict the battlefield situation.

引用

页码：369 / 370

页数：2

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