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Covariance bounds for augmented state Kalman filter application

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1 Author(s)
Deaves, R.H. ; Dept. of Adv. Inf. Process., BAe. plc, Bristol, UK

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

Published in:

Electronics Letters  (Volume:35 ,  Issue: 23 )