Information theoretic sensor data selection for active object recognition and, state estimation
Information theoretic sensor data selection for active object recognition and, state estimation
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DOI：
10.1109/34.982896
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发表时间：
2002-02-01
影响因子：
23.6
通讯作者：
Brown, CM
中科院分区：
文献类型：
--
作者：
Denzler, J;Brown, CM
本文介绍了静态系统迭代状态估计中最优传感器参数选择的一种形式。我们的最优性标准是减少状态估计过程中的不确定性，而不是估计器特定的度量（例如，最小均方估计误差）。其主张是，如果可以减少估计过程中的不确定性和模糊性，则状态估计变得更可靠。我们使用香农的信息理论来选择最大化互信息的信息收集动作，从而优化数据所传达的关于系统真实状态的信息。该技术明确地考虑到先验概率的互信息的计算。因此，可以通过将决策过程中某个时间步的先验概率处理为前一个时间步的后验概率来形成顺序决策过程。我们证明了我们的方法的好处，在物体识别应用程序中使用的主动相机顺序注视控制和视点选择。我们描述了离散和连续密度表示的实验，表明该方法的有效性。
We introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. Our optimality criterion is the reduction of uncertainty in the state estimation process, rather than an estimator-specific metric (e.g., minimum mean squared estimate error). The claim is that state estimation becomes more reliable if the uncertainty and ambiguity in the estimation process can be reduced. We use Shannon's information theory to select information-gathering actions that maximize mutual information, thus optimizing the information that the data conveys about the true state of the system. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object recognition application using an active camera for sequential gaze control and viewpoint selection. We describe experiments with discrete and continuous density representations that suggest the effectiveness of the approach.