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Indoor positioning using exclusively PIR sensors
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AI
DOI：10.1080/18824889.2025.2504741.png)
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Abstract
En 中文
This paper proposes a method for indoor person tracking using only time-series data obtained from multiple PIR (Passive Infrared) sensors. The proposed method assumes a person is near the boundary of the sensor's detection range when the data from a PIR sensor changes. Assuming a constant speed of movement, the method utilizes the proportional relationship between the number of obtained sensor data points and the distance covered during that period to estimate the person's movement path. The effectiveness of the proposed method was verified through simulations where a person moves at a constant speed in environments with multiple sensors. At regular intervals, each sensor detects the presence or absence of a person and outputs a binary signal (1 or 0). By integrating the proposed method into Monte Carlo simulations, the estimation of the person's movement path was performed. Finally, the following implications were revealed: (i) When a person moves within the overlapping area of sensors, paths closely approximating the true path can be calculated. However, (2) candidate points exist in proximity to the sensor detection range boundary, and in the proposed method, where candidate point generation is limited to the detection range boundary, evaluation scores for correct candidate paths may not always be high. Additionally, (3) in cases where sensor detection ranges do not overlap, accurately determining candidate routes becomes challenging.
Keywords:
Indoor positioning
Monte Carlo simulation
multiple sensors
PIR sensor
time-series data
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0.5
Papers:
46
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