Quality assurance / quality control analysis of dead reckoning parameters in a personal navigator

Abstract

The personal navigator (PN) prototype, developed at The Ohio State University Satellite Positioning and Inertial navigation (SPIN) Laboratory, integrates GPS, tactical grade inertial measurement unit (IMU), digital magnetometer compass, digital barometer, and uses a human locomotion model to support dead reckoning (DR) navigation for rescue work, security and emergency services, police safety and military applications. The human locomotion model is represented here by the step length (SL) and step direction (SD). In the absence of GPS signals, the SL is predicted by a knowledge-based system (KBS) in the form of artificial neural network (ANN) and fuzzy logic (FL), while the SD is directly measured by the magnetometer and gyro IMU and modeled by a special module of a Kalman Filter, referred to as DR-KF. If the duration of a GPS outage is prolonged, the gyro and to some extent the magnetometer sensor errors will increase due to lack of updated calibration parameters that may result in an unacceptable level of navigation error. The current target accuracy of the system is 3–5 m circular error probable, 50% (CEP), where the navigation performance depends predominantly on the quality of SD estimation. In this paper, a quality assurance/quality control (QA/QC) mechanism is introduced to test methodologies that predict SL and SD parameters and to monitor their integrity during DR navigation. The QA/QC process proposed here includes fully automated data processing for verification of the measurement and other data content acquiescence and detecting outliers, essential for rejecting incorrectly attributed DR parameters; all these processes are performed in real-time. If the QA/QC analysis rejects the quality of the predicted DR parameters, particularly the SD value, the system may cease operation and issue a warning message requiring the recalibration of sensors. To test the algorithm, in this paper, a performance analysis in the indoor environments is discussed. personal navigator, quality assurance / quality control, knowledge-based system, data validation