Nonlinear Least Square Based on Control Direction by Dual Method and Its Application

Abstract EN

A direction controlled nonlinear least square (NLS) estimation algorithm using the primal-dual method is proposed.

The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality.

The iterative algorithm is designed.

The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate.

The main research method is to combine qualitative analysis and quantitative analysis.

The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis.

The root mean square error (RMSE) is used for quantitative analysis.

The results of experiment show that the model has the smallest residual error and the minimum root mean square error.

The new estimate model has effectiveness and high precision.

The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.