Classification of radar signal features in electronic warfare with convolutional long-short time memory
Mustafa Atahan Nuhoglu · 2018
Radar signals are time series that have pulse repetition interval, pulse width and pulse amplitude as their features. After reception of them by electronic warfare systems, their features are classified and kept in a database. This procedure brings vision for the system user if the same signal is received again in the future. For this classification purpose, three algorithms were implemented. The first one is a combined network consisting of a Convolutional Neural Network (CNN) and a Long-Short Time Memory (LSTM), the second is a Hybrid Network including the first network and a CNN which enables parallel training having histogram data as its input. The last algorithm is Stacked Autoencoder. Performance analysis was made on real radar data. The best performer is Hybrid Network which reached 99.6% accuracy as it involves histogram data usage and an LSTM for the time series problem. Convolutional LSTM reached 98.3% while Stacked Autoencoder had 87% accuracy.