This method optimizes the sparse autoencoder at the beginning and then extracts the deep pulse feature of the radar signal using the coding layer parameters automatically. The support vector machine is used to classify and identify the typical radiation source signals characterized by deep pulse features.
Aug 27, 2021
The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier and verifies that ...
Aug 27, 2021 · Second, by optimizing the sparse autoencoder and confirming the training scheme, intrapulse deep features are autoextracted with encoder layer ...
The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier. The experimental ...
First, this method gets the sparse autoencoder by adding certain restrain to the autoencoder. Second, by optimizing the sparse autoencoder and confirming...
Dec 31, 2021 · In this paper, we propose a new method to extract the characteristics of radar emission signals. This method first reconstructs the radar transmitter signal ...
We propose a radar pulse sequence's feature extraction method based on distribution information, representing complex REPS data as concise 2D feature maps.
The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model.
This research addresses the challenge by exploring three artificial intelligence (AI)-driven AMRS architectures for identifying phase-coded waveforms.
Mar 29, 2022 · In this study, a feature analysis and extraction method was proposed for specific emitter identification based on the signal generation mechanisms of radar ...