Resampling is a critically operation to solve degeneracy problem with particle filters.
重采样是解决粒子滤波退化问题的主要方法。
Aiming at the choice of proposal function and degeneracy problem in particle filter, an improved algorithm is put forward.
针对粒子滤波算法建议性函数的选择问题和粒子匮乏现象，提出了改进粒子滤波算法。
The structure of the formula is simple and easy to compute, the linear system method is superior to the former for handling the degeneracy problem.
退化现象是应用粒子滤波算法的一个主要障碍，常规的再采样方法虽然可解决退化问题，但容易产生粒子耗尽现象。
Discuss the Sequential Monte Carlo localization method for wireless sensor networks scheme and modify the basic algorithm to overcome the sample degeneracy problem in resampling stage.
讨论了贯序蒙特卡罗方法在无线传感器网络节点定位算法中的实现，并针对再采样阶段的样本缺失现象，对基本算法进行了改进。
As the non-degeneracy condition holds and the smoothing parameter tends to zero, an S-stationary point of the MPCC problem is equivalent to a KKT point of the smoothing nonlinear programming.
当非退化条件成立和磨光参数趋于零时，证明了原问题的S -稳定点与磨光非线性规划的KKT点等价。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再采样粒子滤波虽可缓解粒子退化，但易导致样本贫化；扩展粒子滤波也可在一定程度上解决退化问题，但难以跟踪突变状态。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再采样粒子滤波虽可缓解粒子退化，但易导致样本贫化；扩展粒子滤波也可在一定程度上解决退化问题，但难以跟踪突变状态。
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