neural networks

target recognition

automatic modulation

apoptosis_53394

point cloud data

convolutional neural network

modulation classification

modulation recognition

hrrp

micro-doppler_70078

ckd_114670

signal recognition

happ

movement_123462

tidal energy

data matrix

IEEE Communications Letters

In modern electronic warfare environments, there are multiple-radar transmitting signals. For an electronic support system, it is essential to recognize the modulation of pulse repetition intervals (PRIs), since it is directly related to the indication of radar emitters. However, PRI modulations are more difficult to recognize in modern electronical environments due to the high ratio of lost and spurious pulses. Therefore, a fully automatic approach for recognizing seven PRI modulation types using a convolutional neural network (CNN) is proposed in this letter. Simulation results show that our CNN-based recognition method not only promotes performance but is also robust to the environment with lost and spurious pulses. The recognition accuracy is 96.1% with 50% lost pulses and 20% spurious pulses in simulation scenario.

Toward Convolutional Neural Networks on Pulse Repetition Interval Modulation Recognition