A direct algorithm for joint optimal sensor scheduling and MAP state estimation for hidden Markov models
A direct algorithm for joint optimal sensor scheduling and MAP state estimation for hidden Markov models
复制标题
隐马尔可夫模型联合最优传感器调度和MAP状态估计的直接算法
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Douglas L. Jones
中科院分区：
文献类型：
--
作者：
David M. Jun;David M. Cohen;Douglas L. Jones
具有多个传感器和工作模式的传感系统需要主动管理技术来平衡估计质量和测量成本。已有文献表明，在隐马尔可夫模型的联合传感器调度和状态估计问题中，估计器的优化可以独立于每个时间步的调度器来完成。我们研究了当使用MAP估计时的特殊情况，并展示了如何将联合问题转化为标准的部分可观测的MarkovDecision过程(POMDP)，从而使我们能够使用POMDP求解器。由于这种方法是高度冗余的，我们得到了一个直接解，它利用了可分性，同时仍然使用标准求解器。与标准技术相比，直接算法节省了状态空间维度的一倍。以野生动物监测为例，给出了数值结果。
Sensing systems with multiple sensors and operating modes warrant active management techniques to balance estimation quality and measurement costs. Existing literature shows that in the joint sensor-scheduling and state-estimation problem for HMMs, estimator optimization can be done independently of the scheduler at each time step. We investigate the special case when a MAP estimator is used, and show how the joint problem can be converted to a standard Partially Observable MarkovDecision Process (POMDP), which in turn enables us to use POMDP solvers. As this approach is highly redundant, we derive a direct solution, which exploits the separability property while still utilizing standard solvers. When compared to standard techniques, the direct algorithm provides savings by a factor of the state-space dimension. Numerical results are given for an example motivated by wildlife monitoring.