SECOND ORDER REGRESSION PROCESSES IN GEOMETRIC SATELLITE DATA REDUCTION PROBLEMS,
Abstract
In a pure geometric approach to three dimensional satellite data reduction processes, we develop a system of normal equations which can have either of two forms. These forms evolve from the assumptions that only random errors exist in the data or that the data also contains systematic error. If we consider only random errors and carry satellite positions as unknown parameters, we obtain what we refer to as a first order, partitioned, regression system. On the other hand, if we assume that systematic errors exist in the data as well as random errors and make further assumptions that the systematic errors are different on each satellite pass, we obtain what we refer to as a second order, partitioned, regression system. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1968
- Accession Number
- AD0837119
Entities
People
- Lawrence A. Gambino