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An Auxiliary Particle Filtering Algorithm With Inequality Constraints

机译:具有不等式约束的辅助粒子滤波算法

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摘要

For nonlinear non-Gaussian stochastic dynamic systems with inequality state constraints, this technical note presents an efficient particle filtering algorithm, constrained auxiliary particle filtering algorithm. To deal with the state constraints, the proposed algorithm probabilistically selects particles such that those particles far away from the feasible area are less likely to propagate into the next time step. To improve on the sampling efficiency in the presence of inequality constraints, it uses a highly effective method to perform a series of constrained optimization so that the importance distributions are constructed efficiently based on the state constraints. The caused approximation errors are corrected using the importance sampling method. This ensures that the obtained particles constitute a representative sample of the true posterior distribution. A simulation study on vehicle tracking is used to illustrate the proposed approach.
机译:对于具有不等式约束的非线性非高斯随机动态系统,本技术说明提出了一种有效的粒子滤波算法,约束辅助粒子滤波算法。为了处理状态约束,所提出的算法概率地选择粒子,使得远离可行区域的那些粒子不太可能传播到下一时间步长。为了在不等式约束存在的情况下提高采样效率,它使用一种高效的方法来执行一系列约束优化,以便根据状态约束有效地构造重要性分布。使用重要性采样方法校正引起的近似误差。这确保了所获得的粒子构成真实后验分布的代表性样本。通过对车辆跟踪的仿真研究来说明所提出的方法。

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