Scenario-Dependent ZUPT-Aided Pedestrian Inertial Navigation with Sensor Fusion

Pedestrian navigation has been of high interest in many fields, such as human health monitoring, personal indoor navigation, and localization systems for first responders. Due to the potentially complicated navigation environment, selfcontained types of navigation such as inertial navigation, which do not depend on external signals, are more desirable. Pure inertial navigation, however, suffers from sensor noise and drifts and therefore is not suitable for long-term pedestrian navigation by itself. Zero-velocity update (ZUPT) aiding technique has been developed to limit the navigation error growth, but adaptivity of algorithms, model fidelity, and system robustness have been major a concern if not properly addressed. In this paper, we attempt to establish a common approach to solve the problem of self-contained pedestrian navigation by identifying the critical parts of the algorithm that have a strong influence on the overall performance. We first review approaches to improve the navigation accuracy in each of the critical part of implementation proposed by other groups. Then, we report our results on analytical estimations and experiments illustrating effects of combining inertial sensor calibration, stance phase detection, adaptive model selection, and sensor fusion.