A big catalyst of the AI revolution has been Artificial Neural Networks (ANN), abstract computation models based on the biological neural networks in the brain. However, they require an immense amount of computational resources and power to configure and when deployed often are dependent on cloud resources to function. This makes ANNs less suitable for edge computing devices where all these resources are scares. Spiking Neural Networks (SNN) are a new generation of neural networks which process information via sparse discrete time events, called "spikes". When mapped to neuromorphic hardware, SNNs promise high energy efficiency and low computational latency. This work proposes a SNN classification architecture, using it to classify radar based hand gesture signatures. Literature on this topic is limited, leading us to explore certain ANN topologies to test our assumptions. By considering additional design limitations, we aim to find a neuromorphic hardware compatible design. While the proposed architecture is still limited in terms of classification accuracy. Our experiments have exposed interesting relationships between network sizes, accuracy and dimensionality reduction in SNNs.

A system has been developed that utilises the techniques of Ultra Wideband and Synthetic Aperture Radar to produce top view images of a scene using measurements from the side. The system consists of the PulsON P410 radar module, a set of antennas, a moving platform and an imaging algorithm. This thesis will cover all the aspects of the data acquisition part of the system with additionally an analysis on antennas.