Bayesian Two-Stage Sequential Change Diagnosis Via Multi-Sensor Array
Bayesian Two-Stage Sequential Change Diagnosis Via Multi-Sensor Array
复制标题
通过多传感器阵列进行贝叶斯两阶段顺序变化诊断
DOI：
10.1109/mlsp52302.2021.9596446
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Shuguang Cui
中科院分区：
文献类型：
--
作者：
Xiaochuan Ma;L. Lai;Shuguang Cui
本文提出并解决了多传感器环境下的两阶段贝叶斯序列变化诊断问题。在考虑的问题中，变化在传感器阵列中逐渐传播。在检测到变化后，我们可以继续观察更多的样本，以便更准确地识别变化后的分布。目标是最小化总成本，包括延迟、误报和误诊概率。我们描述了最优SCD规则。此外，为了解决最优SCD规则的高计算复杂度问题，我们提出了一个低复杂度阈值规则，该规则在单位延迟成本趋于零时是渐近最优的。
In this paper, we formulate and solve a two-stage Bayesian sequential change diagnosis (SCD) problem in a multi-sensor setting. In the considered problem, the change propagates across the sensor array gradually. After a change is detected, we are allowed to continue observing more samples so that we can identify the distribution after the change more accurately. The goal is to minimize the total cost including delay, false alarm, and misdiagnosis probabilities. We characterize the optimal SCD rule. Moreover, to address the high computational complexity issue of the optimal SCD rule, we propose a low-complexity threshold rule that is asymptotically optimal as the unit delay costs go to zero.