A Modified Bayesian Filter for Randomly Delayed Measurements
A Modified Bayesian Filter for Randomly Delayed Measurements
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DOI：
10.1109/tac.2016.2531418
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发表时间：
2017-01-01
影响因子：
6.8
通讯作者：
Bhaumik, Shovan
中科院分区：
文献类型：
--
作者：
Singh, Abhinoy Kumar;Date, Paresh;Bhaumik, Shovan
在离散时间系统中，传统的贝叶斯近似滤波框架假定测量在每个时刻都可用。但在实践中，测量可能会随机延迟。在文献中，已经研究过这个问题，并通过将最大延迟次数限制在一个或两个时间步长来提供解决方案。本技术说明开发了一种处理测量中具有任意数目延迟的滤波问题的方法。为了实现这一目标，对传统的贝叶斯逼近非线性滤波问题进行了改进，重新表述了测量更新过程中出现的均值和协方差的表达式。我们使用体积求积规则来计算在改进的滤波算法中出现的均值向量和协方差矩阵的多元积分表达式。在两个不同的例子中，我们将考虑延迟的新算法与现有的CQKF启发式算法进行了比较，并展示了考虑随机延迟的算法如何改善滤波性能。
The traditional Bayesian approximation framework for filtering in discrete time systems assumes that the measurement is available at every time instant. But in practice, the measurements could be randomly delayed. In the literature, the problem has been examined and solution is provided by restricting the maximum number of delay to one or two time steps. This technical note develops an approach to deal with the filtering problems with an arbitrary number of delays in measurement. Pursuing this objective, traditional Bayesian approximation to nonlinear filtering problem is modified by reformulating the expressions of mean and covariances which appear during the measurement update. We use the cubature quadrature rule to evaluate the multivariate integral expressions for the mean vector and the covariance matrix which appear in the developed filtering algorithm. We compare the new algorithm which accounts for delay with the existing CQKF heuristics on two different examples and demonstrate how accounting for a random delay improves the filtering performance.