Indoor positioning using exclusively PIR sensors
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2025.2504741 |
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| _version_ | 1849422200802639872 |
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| author | Masayuki Otani Ryouya Tanigawa Hitoshi Habe Koji Abe Nobukazu Iguchi |
| author_facet | Masayuki Otani Ryouya Tanigawa Hitoshi Habe Koji Abe Nobukazu Iguchi |
| author_sort | Masayuki Otani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e3dfc7b83ed43209047fcb3de3c84b5 |
| institution | Kabale University |
| issn | 1884-9970 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj-art-2e3dfc7b83ed43209047fcb3de3c84b52025-08-20T03:31:11ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702025-12-0118110.1080/18824889.2025.25047412504741Indoor positioning using exclusively PIR sensorsMasayuki Otani0Ryouya Tanigawa1Hitoshi Habe2Koji Abe3Nobukazu Iguchi4Kindai UniversityKindai UniversityKindai UniversityKindai UniversityKindai UniversityThis 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.http://dx.doi.org/10.1080/18824889.2025.2504741indoor positioningmonte carlo simulationmultiple sensorspir sensortime-series data |
| spellingShingle | Masayuki Otani Ryouya Tanigawa Hitoshi Habe Koji Abe Nobukazu Iguchi Indoor positioning using exclusively PIR sensors SICE Journal of Control, Measurement, and System Integration indoor positioning monte carlo simulation multiple sensors pir sensor time-series data |
| title | Indoor positioning using exclusively PIR sensors |
| title_full | Indoor positioning using exclusively PIR sensors |
| title_fullStr | Indoor positioning using exclusively PIR sensors |
| title_full_unstemmed | Indoor positioning using exclusively PIR sensors |
| title_short | Indoor positioning using exclusively PIR sensors |
| title_sort | indoor positioning using exclusively pir sensors |
| topic | indoor positioning monte carlo simulation multiple sensors pir sensor time-series data |
| url | http://dx.doi.org/10.1080/18824889.2025.2504741 |
| work_keys_str_mv | AT masayukiotani indoorpositioningusingexclusivelypirsensors AT ryouyatanigawa indoorpositioningusingexclusivelypirsensors AT hitoshihabe indoorpositioningusingexclusivelypirsensors AT kojiabe indoorpositioningusingexclusivelypirsensors AT nobukazuiguchi indoorpositioningusingexclusivelypirsensors |