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Radar Working Mode Recognition Algorithm Based on Recurrent Neural Networks

Published: 03 May 2024 Publication History

Abstract

In response to the recognition problem of multi-functional radar working modes in complex battlefield environments, a radar working mode recognition algorithm based on recurrent neural networks (RNNs) is proposed. This algorithm takes the original data of radar working modes as input and leverages the ability of RNNs to effectively recognize temporal correlation features of input signals. It avoids the factors of noise influence during feature extraction in traditional methods, enabling the discovery of more representative features from the original data and achieving effective recognition of radar working modes. Four different types of recurrent neural network models were used to recognize the raw data of radar working modes. The experiments demonstrated that RNNs are capable of recognizing radar working mode raw data with noise.

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  1. Radar Working Mode Recognition Algorithm Based on Recurrent Neural Networks

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    SPCNC '23: Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications
    December 2023
    435 pages
    ISBN:9798400716430
    DOI:10.1145/3654446
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 May 2024

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