Course title

Course code

Syllabus

Adjustment Computation for Geoinformatics-II

         CE771a

Review of Least squares (LS): Adjustment of observations using observation equations, condition equations and combined equations form

Variations to LS methods: LS with constraints, Bayesian LS, treatment of nuisance parameters

Adjustment using generalized LS

Datum problem and free network adjustment, rank deficient models

least squares collocation

Dynamic Mode Filtering and Prediction:Prediction, Filtering, and Smoothing, sequential/recursive/phased adjustment, stacking of normal equations, Helmert-Wolf blocking, Kalman Filtering, comparison of Kalman filter and LS, Similarity (S) transformation, deformation analysis.

Applications: Geodesy, Photogrammetry, GNSS, 3D Network adjustment

 

References

  1. Ghilani C. D., 2010. Adjustment Computations: Spatial Data Analysis (5th ed.), Wiley: NJ, pp. 647.
  2. Krakiwsky, E. J. 1994, A synthesis of recent advances in the method of least squares, Lecture notes 42, Department of Geodesy and Geodetic Science, University of New Brunswick.
  3. Leick, A., Rapoport, L., Tatarnikov, D., 2015. GPS Satellite Survey (4th ed.), Wiley: NJ, pp. 836.
  4. Mikhail E. M. and Ackermann F., 1976. Observations and Least  Squares, IEP Dun-Donnelley: NY, pp. 497.
  5. Moritz, H, 1972, Advanced Least-squares methods, report no. 175, Department of Geodetic Science, Ohio State University.
  6. Ogundare, J. O. 2019, Understanding Least Squares Estimation and Geomatics Data Analysis, Wiley: USA, pp. 697.
  7. Ogundare, J. O. 2019, Understanding Least Squares Estimation and Geomatics Data Analysis, Wiley: USA, pp. 697.