Jamming Prediction for Radar Signals Using Machine Learning Methods

Table 1

Parameters of a LSTM model.

Step	Parameters	Tested values	Selected value	Note
1	Optimization method	RMSProp, Adam, Adadelta	Adam	Initial learning rate was set as 0.003 for RMSProp [23], Adam [24], and 1.0 for Adadelta [25]
2	Minibatch size	50, 100, 200	200
3	Dropout ratio (%)	0, 10, 30, 50	0	Dropout ratio means the rate at which the output gate units in a LSTM layer are randomly removed
4	LSTM layers	1, 2	2	The model with 2 LSTM layers showed higher accuracy than the model of 1 LSTM layer
5	Fully connected layer	0, 1	1	The model with the fully connected layer had a higher accuracy than the model with no fully connected layer
6	Input features	3, 5	3	Higher accuracy was obtained when using three features, RF, PRI, and PW, instead of using 5 features, AOA, AMP, RF, PRI, and PW
7	Decay ratio	Use, no use	Use	When gradually decreasing the learning rate by multiplying 0.9 to the previous learning rate per epoch after epoch 10, higher accuracy was obtained