Abstract: There has been a growing interest in employing infrared (IR) detectors to locate and track multiple sources in recent years. Conventional methods of locating point sources such as centroiding are not effective when point sources are closely spaced. In this paper, alternative location finding methods with the potential of resolving closely spaced objects (CSOs) is introduced. Of the three algorithms introduced here, two are based on the eigendecomposition of the input data. The other is predicated on least squares error modeling (LSE) with a Gram-Schmidt orthogonalization step to ensure fast convergence. Resolution capabilities of these algorithms are compared through Monte Carlo simulations at various noise levels. Estimates obtained through the LSE modeling approached the Cramer-Rao lower bound for high signal-to-noise-ratios. The performance of the LSE estimate is severely degraded in the presence of nongaussian noise. An outlier detection scheme that may be used in conjunction with the location and amplitude estimation procedure is described. Its effectiveness is demonstrated through Monte Carlo simulations.!13
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