Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters

Abstract

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 PRI values at different time instants are variable. Previous statistical pattern analyzing methods are unable to extract such undetermined PRI values and features, which greatly increases the difficulty of Electronic Support Measures (ESM) against such radars. In this communication, we first establish a model to describe the temporal patterns of software-defined radar pulse trains, then introduce the recurrent neural network (RNN) to mine high-order relationships between successive pulses, and finally exploit the temporal features to predict the time of arrival of upcoming pulses. In the simulation part, we compare different time series prediction models to verify the RNN’s adaptability for pulse sequences of variable parameter radars. Moreover, behaviors of different RNN units in this task are compared, and the results show that the proposed method can learn complex PRI features in pulse trains even in the presence of significant data noises and agile PRIs.

Design of anti‐jamming decision‐making for cognitive radar

“…For example, when active jamming methods such as noise [8], false targets [9], deception waveforms [10] etc. are used, the radar needs to adopt anti‐jamming technologies such as pulse compression [11], phased array antennas [12], Doppler processing [13], variable pulse parameters [14], cognitive radar [15] etc. to improve the signal‐to‐noise ratio, resolution, adaptability etc.…”