Precision laser beam pointing is a key technology in High Energy Laser systems. In this paper, a laboratory High Energy
Laser testbed developed at the Naval Postgraduate School is introduced. System identification is performed and a
mathematical model is constructed to estimate system performance. New beam pointing control algorithms are designed
based on this mathematical model. It is shown in both computer simulation and experiment that the adaptive filter
algorithm can improve the pointing performance of the system.
An algorithm fusion approach is presented to detect a short range target in the land environments with heavy clutters. There has been fundamental problems in extracting the small target from cluttered IR images in a land-based IR Sensor. One of the causes is that we do not have sufficient time to make a decision. In addition, high false alarm and low detection rate make it more difficult to track consecutively without disconnection. It is also found that the target occupies 5 to 7 pixels or more ones in sometimes when it approaches to the counter decision range. When the target is maneuvering the classical matched filter with Gaussian- shaped target assumption generates more false alarm and make it more difficult to track precisely. In order to overcome some of the difficulties, we combined two algorithms. One is the morphological nonlinear filter which is sensitive to the size of the target. The other is the classical matched filter applied to some of the clustered objects. One of the advantages of using morphological filter is that it requires less calculation time than the classical matched filter. It also provides more effective clues when the target is passing the decision range with predetermined target size. In order to reduce the false detection, two dimensional structuring elements are separated to one dimensional elements and applied opening minus closing operation to remove the longitudinal and lateral line objects abundant in the land background. In addition, real time calculation of matched filter based on the object clustering is proposed to implement in real system.
We present a fast parameter estimation method for image segmentation using the maximum likelihood function. The segmentation is based on a parametric model in which the probability density function of the gray levels in the image is assumed to be a mixture of two Gaussian density functions. For the more accurate parameter estimation and segmentation, the algorithm is formulated as a compact iterative scheme. In order to reduce computation time and make convergence fast, histogram information is combined into the algorithm. In the iterative computation, the performance of the algorithm greatly depends on the initial values and properly selected initial estimates make convergence fast. A reasonable approach about the computation of initial parameter is also proposed.
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