Integrity monitoring for all-source navigation enhanced by Kalman filter-based solution separation

Abstract

Integrity is a popular and effective index as a measure of trust for navigation system to place in the correct position. The classical snapshot-based integrity monitoring methods have a widely and mature application in global navigation satellite system (GNSS) assessment. However, they cannot meet the integrity evaluation requirements for multi-sensor integration such as all-source navigation due to its recursive estimation and measurement diversity of sensors, which directly limits it's use in safety-critical applications. We propose a new Kalman filter based solution separation (KFSS) method for the integrity monitoring of multi-sensor integrated navigation systems. The traditional EKF update estimation is remodeled as a weighted least square form to involve the system propagation into the new measurement vector, which reconstructed as a 'pseudo-snapshot' model. The integrity risk caused by the system propagation is considered as one fault hypothesis in the following fault detection and protection level determination. Then, the integrity evaluation is executed in positioning domain enhanced by solution separation with sensor exclusion. The above two operations have indispensable roles and inseparable relationship from the aspect of integrity functional realization. The performance of a tightly coupled integration simulation, a loosely coupled multi-sensor integration simulation and an actual kinematic vehicle experiment verified the feasibility and superiority of the proposed method. The KFSS structure can detect fault in propagation period and step fault, ramp fault and simultaneous faults in observations effectively. The protection levels can be reduced positively both in horizontal and vertical directions, which is positive to bound the position error more accurately and reduce the redundant space effectively. It is of great significance for tighter integrity requirements.