An Algorithm in Adjustment Model with Uncertainty

doi:10.11947/j.AGCS.2017.20160522

Abstract

Abstract: The uncertainty of observation often affects the validity of parameter estimation, and the effects of uncertainty can be reduced effectively by incorporating uncertainty into the adjustment model as an observation error parameter. An adjustment criterion is proposed under the bound constrain of uncertainty, in which the sum of squares of random error and uncertainty error should be minimized, and provided an iteration algorithm to solve the adjustment model. With simulation examples, the estimation results of uncertainty least-square method are compared with that of total least-square method. The results show that the estimation results of uncertainty least-square method are better than that of total least-square method to a certain extent and more applicable when uncertainty is greater.

Key words: adjustment model, adjustment criterion, uncertainty, total least-squares estimation, prior information

CLC Number:

P207

WANG Zhizhong, CHEN Danhua, SONG Yingchun. An Algorithm in Adjustment Model with Uncertainty[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(7): 834-840.

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