Linear and bayesian planet detection algorithms for the terrestrial planet finder

Current plans call for the first Terrestrial Planet Finder mission, TPF-C, to be a monolithic space telescope with a coronagraph for achieving high contrast. The coronagraph removes the diffracted starlight allowing the nearby planet to be detected. In this paper, we present a model of the planet measurement and noise statistics. We use this model to develop two planet detection algorithms, one based on matched filtering of the point-spread function (PSF) and one using Bayesian techniques. These models are used to formulate integration time estimates for a planet detection with desired small probabilities of false alarms and missed detections.