Bayes Filter-Based Occupancy Detection Using Networked SLEEPIR Sensors
Bayes Filter-Based Occupancy Detection Using Networked SLEEPIR Sensors
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DOI：
10.1109/jsen.2023.3304372
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发表时间：
2023-10
影响因子：
4.3
通讯作者：
Muhammad Emad-ud-din;Zhangjie Chen;Qijie Shen;Libo Wu;Ya Wang
中科院分区：
文献类型：
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作者：
Muhammad Emad-ud-din;Zhangjie Chen;Qijie Shen;Libo Wu;Ya Wang
我们之前已经开发了一种同步低能量电子斩波被动红外(SLEEPIR)传感器节点，可以检测静止和移动的乘员。在本文中，我们提出了一种基于贝叶斯滤波(BF)的网络级算法，该算法使用部署在住宅公寓中的SLEEPIR传感器节点网络来估计整个公寓的入住率。该方法通过传感器模型处理来自每个传感器节点的输入观测，并将这些观测转换为贝叶斯更新。传感器模型使用马尔可夫决策过程(MDP)公式来估计从一个占用状态到另一个占用状态之间的占用流速率的时间界限。总的BF输出是表示整个观测空间的占用状态的概率密度函数(PDF)。传感器节点邻接矩阵和观测频率是影响传感器模型设计的关键参数。传感器模型使用估计的占用状态之间的转移时间和概率来过滤不符合参数规定的约束的观测。通过应用于BF的输出PDF的阈值函数来建立占用率。使用SLEEPIR传感器系统在一个住宅单元收集了一个月的数据集。结果表明，与单个SLEEPIR节点提供的准确度状态相比，占用准确率平均提高了23.68%。结果还表明，与已提出的基于粒子滤波(PF)的占用估计算法确定的精度状态相比，占用精度提高了7.74%。
We have previously developed a synchronized low-energy electronically chopped passive infrared (SLEEPIR) sensor node that can detect both stationary and moving occupants. In this article, we present a Bayes filter (BF)-based network-level algorithm that uses a network of SLEEPIR sensor nodes deployed at a residential apartment to estimate the occupancy of the entire apartment. The method processes the incoming observations from each of the sensor nodes via a sensor model and transforms these observations into Bayesian updates. The sensor model uses a Markov decision process (MDP) formulation to estimate the temporal bounds on the rate of occupancy flow between one occupancy state to another. The overall BF output is a probability density function (pdf) that represents the occupancy state of the entire observed space. The sensor node adjacency matrix and observation frequency are the key parameters that contribute to the sensor model design. The sensor model uses estimated transition time and probability between occupancy states to filter out observations that do not conform to the constraints set forth by the parameters. Occupancy is established through a thresholding function applied to the output pdf of the BF. A dataset was collected at a residential unit over a period of one month using the SLEEPIR sensor system. Results indicate an average 23.68% occupancy accuracy improvement when compared to the accuracy state delivered by individual SLEEPIR nodes. Results also indicate a 7.74% occupancy accuracy improvement when compared to the accuracy state determined by an already proposed particle filter (PF)-based occupancy estimation algorithm.