Engineering topics
Publications and source records attributed to Argentiero, P..
This report studies the feasibility of employing satellite to satellite tracking in lieu of ground based tracking to satisfy the orbit determination requirements of the GEOS-C mission. It is shown that with proper estimation procedures it is possible to obtain from S.S.T. data a GEOS-C orbit whose altitude error averages about 1 meter. The usefulness of this data type for geopotential recovery is also indicated.
The feasibility of employing satellite to satellite tracking in lieu of ground based tracking to satisfy the orbit determination requirements of the GEOS-C mission was studied. It is shown that with proper estimation procedures it is possible to obtain from S.S.T. data a GEOS-C orbit whose altitude error averages about 1 meter. The usefulness of this type for geopotential recovery is also indicated.
In processing altimeter data from a spacecraft borne altimeter to estimate the fine structure of the marine geoid, a problem is encountered. In order to describe the geoid fine structure, a large number of parameters must be employed and it is not possible to simultaneously estimate all of them. Unless the parameterization exhibits good orthogonality in the data, serious aliasing results. From simulation studies it has been found that amongst several competing parameterizations, the mean free air gravity anomaly model (i.e., Stokes' formula) exhibited promising geoid recovery characteristics. Using covariance analysis techniques, this report provides quantitative measures of the orthogonality properties associated with the above mentioned parameterization. It has been determined that a 5 deg x 5 deg area mean free air gravity anomaly can be estimated with an uncertainty of 1 mgal (40 cm undulation) provided that all free air gravity anomalies within a spherical radius of 10 arc degrees are simultaneously estimated.
A procedure for applying covariance analysis to determine the most efficient estimation strategy for satisfying the stringent mission requirements of long arc orbit determination of applications satellites is presented. The procedure is applied to the problem of satisfying mission requirements with respect to altitude determination of GEOS-C. It is shown that requirements are met when twelve dominant geopotential coefficients are estimated along with satellite state. This application of covariance analysis is general and can be applied to future applications satellites. Recommendations for future studies are also given.
The accuracy with which the GEOS-C altitude may be estimated over long (7 day) and short (40 minute) orbital arcs is investigated. Over the long are excellent agreement was attained between a simulation of the orbit determination process and a covariance analysis. Both approaches yielded RMS altitude errors of about 1.5 meters over the Caribbean calibration area and approximately 7.5 meters overall. The geopotential was identified as the largest error source. For the short arc, the covariance analysis revealed that the propagated altitude error is linearly dependent upon station survey component errors which are also the largest source of altitude errors. An Appendix contains the mathematics of covariance analysis as applied to orbit determination.
A technique is presented for processing accelerometer data obtained during the AE missions in order to estimate the atmospheric density profile. A minimum variance, adaptive filter is utilized. The trajectory of the probe and probe parameters are in a consider mode where their estimates are unimproved but their associated uncertainties are permitted an impact on filter behavior. Simulations indicate that the technique is effective in estimating a density profile to within a few percentage points.
An adaptive filter which uses a minimum variance criteria to estimate state noise covariance is presented. It is not necessary to assume white state noise in order to implement the filter. Simulation results are given which demonstrate that the filter tracks a satellite in the presence of modeling errors better than a conventional minimum variance filter with state noise. It is also shown that the propagated convariance matrix of the filter is an accurate indicator of the filter's performance.
A Monte Carlo technique was utilized in order to demonstrate the feasibility of processing in situ measurements of temperature, pressure, and molecular weight. The technique assumes that the lower atmosphere of Venus obeys the ideal gas law and the hydrostatic equation. Time correlations are assumed to exist in the data. It is shown that the errors in trajectory reconstruction are due mostly to noise in the data rather than to inaccuracies in the numerical technique.
Venus spin vector estimation using planetary Explorer spacecraft data
Computer program for calculating visibility intervals between earth tracking station and planetary satellite
Monte Carlo method for obtaining histogram of distribution of minimum fuel midcourse velocity correction of Venus-72 mission