Integrity Monitoring using Gaussian Particle Filtering for Safe Maritime Navigation

Abstract

There are several techniques that can improve the accuracy and/or integrity of GPS by augmentation. The widespread use of differential correction signals from stations using the appropriate maritime radio navigation frequency band between 283.5 and 325 kHz for local augmentation and craft or receiver autonomous integrity monitoring may be mentioned as examples. It is regarded as safety is due to a critical issue for navigation. To ensure the highest level of safety with an acceptable level of service, the International Maritime Organization (IMO) provided the minimum maritime user requirements for a future Global Navigation Satellite System (GNSS) in the Resolution A.915. The requirements are specified in terms of four parameters, accuracy, integrity, availability, and continuity. The requirement parameter that is linked directly to safety is integrity. For safety-critical applications such as harbor entrances and approaches and other waters in which navigation is restricted, it is important to detect and exclude faults that cause accuracy and integrity risks, so that the navigation system can operate continuously without performance degradation. For high accurate systems, the integrity monitoring function needs to detect and exclude small biases. And it also estimates protection level that determines availability of the navigation system.

Because the Kalman filter presumes that measurement noise and disturbance follow the Gaussian distribution, its performance might degrades if the assumption is not right. To solve this problem, this paper proposes a fault detection and exclusion algorithm using particle filters. The particle filters are popular filtering methods to estimate states of a general dynamic system. It can deal with any system non-linearities or any noise distributions using sequential Monte Carlo method, and present the posterior distributions of the states completely. Because GNSS measurement noise does not follow the Gaussian distribution perfectly