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An iterative smoothing algorithm is developed using Gaussian mixture models in order to tackle challenging nonlinear estimation problems. Gaussian mixture models naturally capture nonlinear and non-Gaussian systems, while smoothing algorithms provide ability to update using measurements obtained in the past. A tree structure and Gaussian distribution splitting method are proposed to mitigate nonlinearity effects and complexities. Two methods, Children Collapsing and Parent Splitting, are developed to utilize sigma-points smoother for Gaussian mixture model. An indoor localization problem is used to explore and validate the approach. Performance of these new methods is compared to a baseline sigma-points smoother, in both simulation and experiment, and shows much improvement in overall error compared to the truth.