Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a range-doppler image that distinguishes each detected object by range and velocity for further processing. A target detection algorithm is used to filter noise and clutter. Each target can be in a region with a different noise level; a simple threshold would yield false positives or miss detections depending on this value. To solve this problem, a Constant False Alarm Rate or CFAR is desirable. A CFAR detector estimates the noise surrounding each target and uses a dynamic threshold based on this.

Spiking Neural Networks are the third generation of Artificial Neural Networks where, instead of continuous signals, the input is encoded into trains of spikes over time. These networks have a potentially efficient hardware implementation instead of the older generation Artificial Neural Networks and could run directly at the sensor edge, lowering latency and power consumption.

This thesis will explore a Spiking Cell Averaging CFAR implementation and attempt to use its desirable properties like a temporal average over multiple radar frames, mimicking the non-coherent integration sometimes done in radar processing. It is shown that some configurations will behave similarly in a simulated environment with additive white gaussian noise.