An Accurate and Robust Indoor Localization System Using Deep Learning and Passive Infrared Sensors

dc.contributor.advisorYongchareon, Sira
dc.contributor.advisorYu, Jian
dc.contributor.authorNgamakeur, Kan
dc.description.abstractInternet of Things (IoT) has evolved significantly over the past decade, enabling a wide range of applications for indoor environments in different fields such as smart building, healthcare, and energy management. A person's location is crucial information in providing their core services. Thus, indoor localization is a fundamental component of these applications. Different technologies are available for indoor localization. However, video camera technology and wearable devices are not always practical in all situations. A video camera may cause privacy issues. People may not cooperate to hold or wear the devices because they forget or feel uncomfortable. As a result, Passive Infrared (PIR) sensors are employed for indoor localization to mitigate these issues. The advantages of PIR sensors include their low power consumption, cost-effectiveness, and low electromagnetic interference. In addition, their analog or voltage outputs can provide fine-grained information regarding a person’s location such as phase and amplitude. Thus, it is possible to leverage analog outputs to estimate a person’s location. However, utilizing PIR analog outputs is not simple due to their ambiguity and a lack of PIR sensor specification. This research focuses on addressing the challenges of indoor localization based on PIR analog signal including the design of a PIR sensor node, single person localization, and multi-person localization. Firstly, this thesis proposes a novel design of a PIR sensor node and a prototype is developed to collect data. Secondly, this thesis proposes a deep learning framework for a single person localization. The proposed CNN-LSTM model not only extracts features from PIR analog output automatically but also learn temporal dependencies between the extracted features. Lastly, the proposed localization framework is extended to support multi-person localization. This thesis proposes a channel separation method to generate inputs for each person. Then, deep CNN-LSTM estimates a location for each person and a mean bagging method is used to integrate multiple CNN-LSTM models for improving the accuracy of multi-person localization. A set of experiments are conducted, and the proposed localization methods can achieve good results for both single person and multi-person localization.en_NZ
dc.publisherAuckland University of Technology
dc.titleAn Accurate and Robust Indoor Localization System Using Deep Learning and Passive Infrared Sensorsen_NZ
dc.typeThesisen_NZ University of Technology Theses of Philosophyen_NZ
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