IEEE - Aerospace and Electronic Systems - April 2023 - 22
LPI Waveform Recognition Using Adaptive Feature Construction and Convolutional Neural Networks
Figure 9.
Accuracy of training and validation in (a) three-layered CNN, (b) five-layered CNN, and (c) seven-layered CNN.
shown in Table 2, to test and compare the performance with
the same dataset. In the training process, the training accuracy
and validation accuracy are plotted in Figure 9 to show the
how well the CNN models fit the dataset. From Figure 9,the
training and validation accuracy ofthe three-layered CNN are
the most similar compared to the other two models, which
means that the three-layered CNN fits better than the other
two more complicated CNN models. In order to exclude the
effect of the small dataset, a double-sized dataset is added to
compare the classification performance. The classification
performance of the three CNN models are compared by testing
signals with SNR level of5, 0, -5, -10, -15 dB. Figure 10
shows the classification accuracy of the three CNN models
with the two datasets. The classification accuracy did not significantly
increase for the three-layered CNN between the two
datasets, nor did the five-layered and seven-layered CNN outperform
the three-layered CNN. Figure 10 indicates that the
three-layered CNN performs better than the other two CNN
structures. Therefore, the three-layered CNN model is chosen
in our system. The other hyperparameters are also determined
based on a similar manual and grid search method. Finally,
the designed LPI waveform recognition system based on the
AFC framework and CNN-based classification module is
shown in Figure 11.
EXPERIMENTAL RESULTS
In this section, evaluations are performed with the simulated
signals for the LPI waveform recognition system
based on the proposed FALPINE system. The LPI waveforms
recognition rates are compared with existing works
using various signal features including WVD [8],
CWD [10], wavelet features [11].
LPI SIGNALS DATA GENERATION
In this work, the simulated LPI signals including polyphase
coded radar signals, such as Frank code, P1 code,
P2 code, P3 code, P4 code, and noise radar signals are
used for the experimental evaluation of the LPI recognition
system. These signals are popularly used and generated
in the literature for the signal classification [1].
Supposing radar sources are disturbed by additive white
Figure 10.
Classification performance of three CNN models with (a) dataset
and (b) double-sized dataset.
22
Gaussian noise (AWGN) and random fading effects in propagation
channel. Different SNR levels ranging from -20 to
5 dB are applied to the generated LPI waveforms. For each
SNR level, in order to compare with the benchmark methods,
IEEE A&E SYSTEMS MAGAZINE
APRIL 2023
IEEE - Aerospace and Electronic Systems - April 2023
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