Covariance bounds for augmented state Kalman filterapplication
Rob H. Deaves · Electronics Letters · 1999
Novel insights into the covariance bounds of an augmented state Kalman filtering (ASKF) application are provided. These are obtained through empirical investigations based on a scenario where a dynamic vehicle senses a static feature for the purpose of mapping that feature and simultaneously localising the vehicle. Numerical results indicate a relationship between the Riccati matrices of the vehicle and feature. Generalisations to multiple features, multiple vehicles and decentralised networks are considered. The relationships derived are applied to a simple system design example.