Abstract:In the process of measuring data processing, the application of reliable prior information can effectively reduce the discomfort of the adjustment model. Based on active set thought, in this paper, using the truncated Newton method, we propose a new algorithm to solve the parameter-bounded adjustment problem. Our algorithm is more efficient than the inequality constraint, with its capacity to change the composition of the active set rapidly, and the precise evaluation of parameter value is given at the same time. Examples are given to show that the algorithm can effectively reduce the unfitness of the model and can maintain the statistical, geometric or physical significance of the parameters.