Improving the reliability of personal navigation devices in harsh environments

With advances in microelectromechanical system (MEMS) technology, many modern personal navigation devices incorporate measurements from various low-cost sensors alongside Global Navigation Satellite Systems (GNSS) receivers. However, both GNSS and other low-cost sensors are prone to the occurrence of faults that are either un-modeled or poorly modeled. This affects the usability of such personal navigation devices in some applications where reliability is a critical parameter. This paper thus presents several algorithms to either detect and remove such faults or model them properly in order to improve performance, especially reliability, for low cost multi-sensor integrated navigation systems. The algorithms presented in this paper can be broadly categorized into two parts. The first part focuses on optimizing the use of GNSS measurements in harsh environments. This is done by replacing the assumption of normal distribution of GNSS measurements with that of a heavy-tailed distribution. Moreover, the covariance of such distribution is also adapted to match the true error characteristics of the surrounding environment with the aid of inertial units. The second part of the algorithm detects possible faults arising in various sensors. Based on the type of sensor fault, the algorithm either rejects some of the measurements before they enter the integration filter, issues a warning signal to indicate lack of reliability information or deems the navigation solution unusable. The analyses of the proposed algorithms showed that faults were detected successfully and that the performance of the navigation system was improved in terms of both reliability and accuracy.