用高斯混合模型进行非线性估计的迭代平滑方法

Daniel J. Lee, M. Campbell
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引用次数: 4

摘要

为了解决具有挑战性的非线性估计问题,提出了一种基于高斯混合模型的迭代平滑算法。高斯混合模型自然捕获非线性和非高斯系统,而平滑算法提供了使用过去获得的测量值进行更新的能力。提出了一种树状结构和高斯分布分裂方法来减轻非线性效应和复杂性。为了使高斯混合模型的sigma点平滑化,提出了子节点坍缩和父节点分裂两种方法。利用室内定位问题对该方法进行了探索和验证。在仿真和实验中,将这些新方法的性能与基线sigma点平滑度进行了比较,结果表明,与真实值相比,这些新方法的总体误差有了很大的改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Iterative smoothing approach using Gaussian mixture models for nonlinear estimation
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.
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