Statistical Orbit Determination

Catalog description

When tracking satellites in orbit, large amounts of tracking data (range, range-rate, azimuth, elevation) is collected. To convert this data to physical orbital elements of the satellite's orbit, this data must be filtered, and this filtering is done using methods of statistical orbit determination. This course focuses on the mathematics and practices in statistical orbit determination for analyzing large amounts of satellite tracking data. Major topics include: classical orbit determination techniques, probability and statistics, least-squares solution, weighted least squares, statistical interpretation of the least-squares problem, Cholesky decomposition, Gauss-Markoff theorem, sequential estimation algorithms, extended sequential estimation algorithms, square root filters, state noise compensation algorithm, state noise compensation algorithms, smoothing algorithms, minimum variance, maximum likelihood, Bayesian estimation. Other topics may be covered as time permits.