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- W4281662743 abstract "In this paper, a Long Short Term Memory (LSTM)-based model combined with a novel normalization method is proposed to determine the range bin position of a helicopter echo in a single Range-Doppler map. To accomplish this goal, the Doppler distribution in each range bin is treated as a sequence and normalized according to the radar parameters. The data is then fed to an LSTM network to perform feature extraction and classification. The proposed method requires less parameters than Convolutional Neural Network-based approaches, is suitable for usage on varying resolution Doppler data and is capable of performing classification on data obtained from radars that were not encountered during training. The model is trained and tested on real-life data obtained from an X-band pulse-Doppler radar and average test accuracies of 85.7% for helicopters and 98.1% for non-helicopters are obtained. To show the effectiveness of the novel normalization method, the same model is tested on a different dataset obtained from another X-band pulse-Doppler radar and average test accuracies of 77.8% for helicopters and 99.8% for non-helicopters are observed." @default.
- W4281662743 created "2022-06-13" @default.
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- W4281662743 date "2022-04-05" @default.
- W4281662743 modified "2023-09-26" @default.
- W4281662743 title "Detection of Helicopters on a Single Range-Doppler Map Using LSTM Networks" @default.
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- W4281662743 doi "https://doi.org/10.23919/eurad50154.2022.9784484" @default.
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