Distributed Cooperative SLAM-based Integrity Monitoring Via a Network of Receivers

In urban areas, the traditional GPS-based Integrity Monitoring (IM) techniques have limitations due to the presence of multipath and satellite blockage that degrades the available data redundancy. We address these urban challenges by exploiting the geographical diversity and added measurement redundancy available via a network of receivers. We propose a Distributed Cooperative (DC) Simultaneous Localization and Mapping (SLAM)-based IM algorithm to compute the position protection levels across a network of receivers. We utilize the UWB ranging and data received from the neighboring vehicles along with the GPS pseudoranges, vehicle dynamics and satellite ephemeris to perform cooperative Graph-SLAM in a distributed manner. For each vehicle, we simultaneously estimate the state vector of the vehicle, neighboring vehicles and GPS satellites via graph optimization. We then estimate the fault status associated with both GPS and UWB measurements by evaluating the residuals against their empirical distribution. Later, we derive the protection levels by calculating the worst-case failure slope for the DC Graph-SLAM framework. Using simulated experiments, we demonstrate that tighter protection levels are achieved via DC SLAM-based IM that utilizes a sparsely connected network of receivers as compared to SLAM-based IM that utilizes a single GPS receiver. We also validate that bounded localization errors are attained across a network of receivers using the proposed DC SLAM-based IM.