论文标题
对传感器网络的最快检测，其变换后分布未知
Quickest Detection over Sensor Networks with Unknown Post-Change Distribution
论文作者
论文摘要
我们建议在传感器网络上提出最快的变更检测问题，在传感器网络中，传感器的子集经历了变化和局部变更后分布尚不清楚。网络中的每个传感器都会在有限字母上观察局部离散时间随机过程。最初，观测值是独立的，并且具有相同的分布（I.I.D。），其已知的前变形分布独立于其他传感器。在固定但未知的变更点，传感器的固定但未知子集发生变化，并开始从未知分布中观察样品。我们假设可以使用分布空间的凹面（或凸）本地统计数据来量化更改。我们提出了一个渐近的最佳和计算障碍的停止时间。在这种情况下，我们提出的方法在融合中心使用凹面的全局累积总和（CUSUM）统计量，并使用信息投影抑制最可能的错误警报。最后，我们显示了所述问题的算法模拟算法的一些数值结果。
We propose a quickest change detection problem over sensor networks where both the subset of sensors undergoing a change and the local post-change distributions are unknown. Each sensor in the network observes a local discrete time random process over a finite alphabet. Initially, the observations are independent and identically distributed (i.i.d.) with known pre-change distributions independent from other sensors. At a fixed but unknown change point, a fixed but unknown subset of the sensors undergo a change and start observing samples from an unknown distribution. We assume the change can be quantified using concave (or convex) local statistics over the space of distributions. We propose an asymptotically optimal and computationally tractable stopping time for Lorden's criterion. Under this scenario, our proposed method uses a concave global cumulative sum (CUSUM) statistic at the fusion center and suppresses the most likely false alarms using information projection. Finally, we show some numerical results of the simulation of our algorithm for the problem described.
