NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION
NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION
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DOI：
10.1049/ip-f-2.1993.0015
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发表时间：
1993-04-01
期刊：
影响因子：
--
通讯作者：
SMITH, AFM
中科院分区：
文献类型：
--
作者：
GORDON, NJ;SALMOND, DJ;SMITH, AFM
提出了一种实现递归贝叶斯滤波器的算法--自举滤波器。所需的状态向量的密度表示为一组随机样本，这些样本由算法更新和传播。该方法不受线性或高斯噪声假设的限制：它可以应用于任何状态转换或测量模型。最后给出了纯方位跟踪问题的仿真实例。该模拟包括用于提高基本算法的效率的方案。对于该示例，自举滤波器的性能大大优于标准扩展卡尔曼滤波器上级。
An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.