Spiking neural networks offer a promising approach for low power edge applications, especially when run on neuromorphic hardware. However, there are no well established approaches to setup such networks for real world applications. We demonstrate the use of spiking neural networks on the basis of radar data-based gesture recognition, while taking three different angle-encoding schemes into account, considering a two antenna based angle estimation. The surrogate gradient approach is used for direct training, while achieving a reasonable accuracy on all proposed encoding schemes. This work proposes a baseline approach for spiking networks and the corresponding encoding for the use in radar-based gesture classification.