On Analysis of Active Querying for Recursive State Estimation
On Analysis of Active Querying for Recursive State Estimation
复制标题
递归状态估计的主动查询分析
DOI：
10.1109/lsp.2018.2823271
复制
发表时间：
2018
影响因子：
3.9
通讯作者：
Akcakaya, Murat
中科院分区：
文献类型：
--
作者：
Kocanaogullari, Aziz;Erdogmus, Deniz;Akcakaya, Murat
在随机线性/非线性主动动态系统中，状态估计与证据通过递归测量响应于系统关于待估计的状态的查询。因此，查询选择是必不可少的，这样的系统，以提高状态估计精度和时间。查询选择通常通过最小化证据方差或优化各种信息理论目标来实现。结果表明，优化的互信息为基础的目标和方差为基础的目标达到相同的解决方案。然而，现有的方法优化近似的预期目标，而不是解决确切的优化问题。为了克服这些缺点，我们提出了一个主动查询过程中使用的互信息最大化递归状态估计。首先，我们表明，互信息推广方差为基础的查询选择方法，并显示目标之间的等价性，如果证据似然单峰分布。然后，我们解决了查询选择的精确优化问题，并提出了一个查询（测量）选择算法。我们专门制定的互信息最大化查询选择作为一个组合优化问题，并表明，目标是次模块化，因此可以有效地解决保证收敛范围通过贪婪的方法。此外，我们分析了查询选择算法的性能测试，通过脑机接口（BCI）打字系统。
In stochastic linear/nonlinear active dynamic systems, states are estimated with the evidence through recursive measurements in response to queries of the system about the state to be estimated. Therefore, query selection is essential for such systems to improve state estimation accuracy and time. Query selection is conventionally achieved by minimization of the evidence variance or optimization of various information theoretic objectives. It was shown that optimization of mutual information-based objectives and variance-based objectives arrive at the same solution. However, existing approaches optimize approximations to the intended objectives rather than solving the exact optimization problems. To overcome these shortcomings, we propose an active querying procedure using mutual information maximization in recursive state estimation. First we show that mutual information generalizes variance based query selection methods and show the equivalence between objectives if the evidence likelihoods have unimodal distributions. We then solve the exact optimization problem for query selection and propose a query (measurement) selection algorithm. We specifically formulate the mutual information maximization for query selection as a combinatorial optimization problem and show that the objective is submodular, therefore can be solved efficiently with guaranteed convergence bounds through a greedy approach. Additionally, we analyze the performance of the query selection algorithm by testing it through a brain computer interface (BCI) typing system.