CUDA Implementation of an Optimal Online Gaussian-Signal-in-Gaussian-Noise Detector

We address the computationally demanding task of real time optimal detection of a Gaussian Signal in Gaussian Noise. The mathematical principles of such a detector were formulated in 1965, but a full real-time implementation of these principles was not possible for decades mainly due to technological barriers. We present a CUDA based implementation of such an optimal detector and study its decision making speed ( or throughput) as function of target signal duration and signal filter length. We also compare the throughput results to those of a CPU based design. We report on detection rates ranging from 3.5 KHz for a target duration of 10756 samples up to 15.6 KHz for target duration of 92 samples. The CUDA based detector running on 384 parallel cores had a superior throughput comparing to a pure CPU implementation when target duration was longer than 600 samples.