As autonomous systems proliferate, empirical measurement of their fitness is paramount. Several frameworks have been developed that provide guidance on what should be measured. However, these frameworks require users to develop their own metrics. Additionally, these frameworks focus on the autonomous systems rather than the enablers. An enabler could be the process used by developers. This research introduces novel techniques to analyze metrics used to measure fitness of autonomy architectures for developers. Crucially, this will be generalizable across autonomy measurement frameworks. The results are new techniques acquisition professionals can use to help better make tradeoffs development-wise for different architectures.
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.
In multi-sensor fusion applications, various sources of data are combined to create a coherent situational picture. The
ability to track multiple targets using multiple sensors is an important problem. The data provided by these sensors can
be of varying quality, such as data from RADAR and AIS. Does this varied quality of data negatively impact the
tracking performance when compared to using the best data source alone? From an information-theoretic standpoint, the
answer would be no. However, this paper investigates this issue and exposes a few caveats. In particular, this study
addresses how the relative update rate of varying quality sensors affects tracking performance and answers the question
'Is more data always better?'
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