Abstract: When correcting for velocity aberration in satellite imagery, a closed-form error covariance propagation model can produce more easily calculable error terms than a corresponding Monte Carlo analysis. The closed-form error covariance propagation model is symbolic, rather than numeric. The symbolic error covariance propagation model relates input parameters to one another pairwise and in closed form. For a particular image, the symbolic error covariance propagation model receives an input measurement value and an input error value for each input parameter. The symbolic error covariance propagation model operates on the input values to produce a set of output correction values, which correct for velocity aberration. The output correction values can be used to convert apparent coordinate values to corrected coordinate values. The symbolic error covariance matrix operates on the input error values to produce a set of output error values, which identify a reliability of the corrected coordinate values.

Abstract: When correcting for velocity aberration in satellite imagery, a closed-form error covariance propagation model can produce more easily calculable error terms than a corresponding Monte Carlo analysis. The closed-form error covariance propagation model is symbolic, rather than numeric. The symbolic error covariance propagation model relates input parameters to one another pairwise and in closed form. For a particular image, the symbolic error covariance propagation model receives an input measurement value and an input error value for each input parameter. The symbolic error covariance propagation model operates on the input values to produce a set of output correction values, which correct for velocity aberration. The output correction values can be used to convert apparent coordinate values to corrected coordinate values. The symbolic error covariance matrix operates on the input error values to produce a set of output error values, which identify a reliability of the corrected coordinate values.