Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9450
• Main Authors: Chen, Xuan-Ying, Wen, Chih-Yu, Sethares, William A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.12.2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Biometrics; data augmentation strategy; Data collection; Data mining; Deep learning; Indoor environments; Internet of Things; Localization; Motion; Motion stability; multiple targets localization; Neural networks; Neural Networks, Computer; non-wearable system; pyroelectric infrared sensors; Sensors; Signal processing; Signal Processing, Computer-Assisted; Systems stability; Universal Serial Bus; Usability; Wearable technology; non-wearable system; data augmentation strategy; deep learning; multiple targets localization; pyroelectric infrared sensors; artificial intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22239450

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.