An evaluation on models for particle filtering for resident tracking in a smart home using binary sensors

Localization and tracking 1 are key components to enable personalized service in smart homes such as lifestyle support, energy management, and safety. To locate a person in a smart home, various sensors are installed. In this work, sensor data from only non Wifi-based devices and sensors such as PIR, pressure mat etc. are considered. The Particle filter has been adopted by many works because it has more advantages, e.g. accuracy, robustness, and efficiency, over the other techniques e.g. Kalman filter. Thus, we aim to analyze a set of motion models including random walk, random waypoint and Gaussian Markov model to find how applicable they are for an indoor setting and how they affect the accuracy of particle filter for indoor location estimation. We also analyze a set of sensor models. The result shows that the chosen motion models are applicable for an indoor environment if their parameters are adjusted properly. Random based models are more appropriate in this setting. However, information, such as behavior, activity, and location etc., should be considered to improve motion models. The accuracy of the particle filter is influenced by both motion and sensor models and their parameters such as walking speeds and degree of randomness, etc. Thus, the inaccurate prediction is produced if an improper model and its parameters are used and it is very difficult to correct. In addition, improper sensor models can cause each particle to be weighted incorrectly. Therefore, the estimation is not accurate.