Trust metrics in information fusion

Erik Blasch

doi:10.1117/12.2050255

28 May 2014 Trust metrics in information fusion

Erik Blasch

Proceedings Volume 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII; 91190L (2014) https://doi.org/10.1117/12.2050255
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust information in the identified imagery track. We use the proportional conflict redistribution rule as an information fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for information fusion trust quantification.

Citation Download Citation

Erik Blasch "Trust metrics in information fusion", Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190L (28 May 2014); https://doi.org/10.1117/12.2050255

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