In tactical operations, maintaining the pedigree of data may be problematic due to limitations in data links. If the pedigree information is missing and/or incomplete, information that arrives at a sensor platform may include information that the platform itself created. If the platform uses this data again, the platform will become incorrectly more confident of this information. This condition is called “self-intoxication,” and it is a system of systems problem. This paper assesses the effects of self-intoxication on the covariance consistency, i.e. how accurately the platform’s track covariance reflects the true uncertainty. We analyze covariance consistency of a track relative to truth.
KEYWORDS: Information fusion, Data fusion, Monte Carlo methods, Sensors, Telecommunications, Detection and tracking algorithms, Data communications, Relays, Mahalanobis distance, Computing systems
This document concerns three information fusion methods: Information Matrix Fusion (IMF), Covariance Intersection (CI), and Sampling Covariance Intersection (SCI). These methods are compared for performance under extreme multiple-counting conditions, that is, when an estimate is improperly fused to a track multiple times as if the estimate was repeatedly found by independent measurements. This situation can possibly occur in networked fusion systems where data pedigree is less than properly maintained, especially when an information relay is implemented to handle diminished communication environments. Extreme multiple-counting behavior in particular is examined for the purposes of this document. This research demonstrates that the normally preferable methods, IMF and SCI, are prone to falsely optimistic covariance values in such situations. All three fusion methods result in the state estimate approaching the estimate being repeatedly fused; the more conservative CI method also results in the covariance approaching that of the repeated estimate. We obtain these results through inference from the governing equations and examination of behavior under Monte Carlo simulations.
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