论文标题
马尔可夫链蒙特卡洛传播阶段的贝叶斯州观察者的近似
An approximation of the Bayesian state observer with Markov chain Monte Carlo propagation stage
论文作者
论文摘要
具有随机不确定性的非线性系统的状态估计问题可以在贝叶斯框架中提出，在贝叶斯框架中，目的是通过其概率密度函数完全替换状态。如果不限制选定的系统类别和干扰属性，贝叶斯估计器对于具有非高斯噪声的高度非线性系统特别有趣。贝叶斯过滤器的主要局限性是较高维度系统的重大计算成本和实施问题。本文引入了马尔可夫链蒙特卡洛传播阶段和核密度估计的贝叶斯状态观察者的分段线性近似。这些方法适合预测多元概率密度函数。分段线性近似和所提出的算法可以以合理的计算成本提高估计性能。在比较贝叶斯状态观察者与扩展的卡尔曼滤波器和粒子滤波器的基准测试中证明了估计性性能。
The state estimation problem for nonlinear systems with stochastic uncertainties can be formulated in the Bayesian framework, where the objective is to replace the state completely by its probability density function. Without the restriction to selected system classes and disturbance properties, the Bayesian estimator is particularly interesting for highly nonlinear systems with non-Gaussian noise. The main limitations of Bayesian filters are the significant computational costs and the implementation problems for higher dimensional systems. The present paper introduces a piecewise linear approximation of the Bayesian state observer with Markov chain Monte Carlo propagation stage and kernel density estimation. These methods are suitable for the prediction of multivariate probability density functions. The piecewise linear approximation and the proposed algorithms can increase the estimation performance at reasonable computational cost. The estimation performance is demonstrated in a benchmark comparing the Bayesian state observer with an extended Kalman filter and a particle filter.
