Maximum Likelihood Detection in Focal Plane Arrays with Generic Point Spread Function (Poster)

In this paper the problem of target detection on images and focal plane arrays (FPA) is considered. The proposed approach has applications in biomedical systems, autonomous surveillance systems, target tracking systems and robotics. In a previous paper at the Fusion conference the problem of single, point target detection on FPA was solved under the assumption that the point spread function of the target is strictly circular. The proposed approach in this paper extends the previous result for a generic point spread function that is more applicable to solve practical problems. In this paper, we derive the maximum likelihood (ML) target detector for image observations; the proposed ML detector is optimal under the generic assumption that the FPA contains a single target that is in the form of a Gaussian signal intensity with known covariance Matrix. Further, we derive the Cramer-Rao lower bound (CRLB) of the estimation and then present the hypothesis test to find a threshold for target acceptance. Finally, we theoretically derive the receiver operating characteristic (ROC) curve of the detector. Simulation results show that the ML estimator is efficient and that the theoretically derived ROC is a close approximation to the realistic one at very low signal to noise ratio values.