Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters
نویسندگان
چکیده
Many modern radars use variable pulse repetition intervals (PRI) to improve anti-reconnaissance and anti-jamming performance. Their PRI features are probably software-defined, but the values at different time instants variable. Previous statistical pattern analyzing methods unable extract such undetermined features, which greatly increases difficulty of Electronic Support Measures (ESM) against radars. In this communication, we first establish a model describe temporal patterns software-defined radar trains, then introduce recurrent neural network (RNN) mine high-order relationships between successive pulses, finally exploit predict arrival upcoming pulses. simulation part, compare series prediction models verify RNN’s adaptability for sequences parameter Moreover, behaviors RNN units in task compared, results show that proposed method can learn complex trains even presence significant data noises agile PRIs.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14215439