Integrity Monitoring on Collaborative Navigation

Collaborative navigation entails a concept of a group of platforms navigating collectively and supporting each other’s positioning solution to obtain higher accuracy and availability for all platforms. It is regarded as a promising solution to meet the demands on accurate positioning and real-time situation awareness in future Intelligent Transportation Systems. Integrity is a measure of trust that can be placed on the information and timely warnings from a navigation system. Integrity monitoring is mostly solved as a hypothesis testing problem in measurement domain and solution domain. This paper explores and investigates the integrity monitoring of collaborative navigation. Two methods are adopted here. The first method is based on the least-squares adjustment of the internodal range measurements to detect any anomalies in GNSS solutions. There is a rank deficiency problem associated with it. Another method is based on Akaike Information Criterion (AIC) that was originally developed for model selection. Tests on partially simulated data sets demonstrate that adjustment methods are effective in detecting GNSS anomalies and better than the innovation-based method for cases with clean range measurements. The AIC based method, however, is able to detect and identify multiple simultaneous GNSS and internodal range outliers.