Indoor Occupancy Estimation Using Particle Filter and SLEEPIR Sensor System

We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor that can detect both stationary and moving occupants by incorporating a liquid crystal (LC) shutter with a traditional passive infrared (PIR) sensor. However, its detection accuracy is still larg...

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Bibliographic Details
Published in:IEEE sensors journal 2022-09, Vol.22 (17), p.17173-17183
Main Authors: Emad-ud-Din, Muhammad, Chen, Zhangjie, Wu, Libo, Shen, Qijie, Wang, Ya
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Background noise
Bayes filters
Correlation
edge computing
Engineering
Estimation
Instruments & Instrumentation
Liquid crystals
Mechanical sensors
Monitoring
Particle filters
passive infrared sensors
Physics
Probability density functions
recurrent neural networks
Sensor systems
Sensors
smart devices
Temperature sensors
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Summary:We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor that can detect both stationary and moving occupants by incorporating a liquid crystal (LC) shutter with a traditional passive infrared (PIR) sensor. However, its detection accuracy is still largely impacted by environmental infrared noises. In this paper, we present a Particle Filter (PF) based system-level algorithm that employs a network of SLEEPIR sensors which are installed at different points of interest within an indoor space. The method interprets the incoming observations from the field of view (FOV) of each sensor via the likelihood function to update the state of the PF. The PF output is a probability density function (pdf) that represents the occupancy state of the entire observed space. The sensor location, observation cone, range, observation frequency and historic inter-sensor correlation are the key parameters that contribute to the likelihood function design. Since the method utilizes the historic correlation among sensors, the pairs of correlating sensors often perform self-correction whenever a faulty observation is encountered due to either sensor limitations or due to environmental noise. Occupancy is established through a thresholding function applied to the output pdf of the PF. A lab-based dataset was collected over a period of 360 hours using the SLEEPIR sensor system. Results indicate an average 8.25% occupancy accuracy improvement when compared to the accuracy state delivered by individual SLEEPIR nodes.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3192270