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

aut.embargoNoen_NZ
aut.filerelease.date2024-07-14
aut.thirdpc.containsNoen_NZ
dc.contributor.advisorYongchareon, Sira
dc.contributor.advisorYu, Jian
dc.contributor.authorNgamakeur, Kan
dc.date.accessioned2022-07-13T22:19:05Z
dc.date.available2022-07-13T22:19:05Z
dc.date.copyright2022
dc.date.issued2022
dc.date.updated2022-07-13T04:05:37Z
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.identifier.urihttps://hdl.handle.net/10292/15298
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.rights.accessrightsOpenAccess
dc.titleAn Accurate and Robust Indoor Localization System Using Deep Learning and Passive Infrared Sensorsen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelDoctoral Theses
thesis.degree.nameDoctor of Philosophyen_NZ
Files
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
889 B
Format:
Item-specific license agreed upon to submission
Description:
Collections