Channel Attention based CNN for Improving UAV Classification using Micro Doppler Spectogram Images.

The increase in technological advancements in unmanned ariel vehicle has lead to the challenges in the detection of drones in flight. The micro Doppler signatures obtained from radars is used to distinguish and detect different types of drones. Due to relatively similar radar spectogram image patterns or micro-Doppler signatures it is sometimes very challenging to classify different types of drones. Previously, Deep Learning methods like transfer learning and residual networks have been proposed to improve the classification accuracy. For further improving the classifying efficiency , this paper investigates the integration of channel attention mechanisms i.e. Squeeze and Excitation Net, Efficient Channel Attention and Gated Channel Transformation in the custom CNN Network (UAVDetect) with three publicly available micro Doppler spectogram UAV datasets. The paper proposes Modified SENet and Modified ECA which further improves the accuracy and better convergence.