MSc CE Thesis Presentation

Spiking CA-CFAR Implementation for Radar Target Detection

Bastiaan van Otterloo

Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a rangedoppler image that distinguishes each detected object by range and velocity to process radar data. A target detection algorithm is used to filter noise and unwanted reflections. Each target can be in a region with different noise levels; a simple threshold will yield false positives or miss detections depending on its value. To solve this problem, a Constant False Alarm Rate or CFAR is desirable.

A CFAR detector estimates the noise surrounding each target and has 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.

Overview of MSc CE Thesis Presentation