Measurement Extraction for a Point Target From an Optical Sensor

This talk considers the measurement extraction for a point target from an optical sensor’s focal plane array (FPA) with a dead zone separating neighboring pixels. Assuming that the energy density of the target deposited in the FPA conforms to a Gaussian point spread function and that the noise means and variance in each pixel are proportional to the pixel area (i.e., according to a Poisson noise model), we derive the Cramer–Rao lower bound (CRLB) for the covariance of ´ the estimated target location. It is observed that there is an optimal pixel size that minimizes the CRLB for a given dead-zone width, and the maximum likelihood estimator is shown to be efficient via Monte Carlo runs for moderate-to-large signal-to-noise ratios. The test statistic for target detection is derived and it is shown to be a matched filter at the estimated location. The distributions of the test statistic under both hypotheses are derived using some approximations. The detection probability is then obtained.