Adaptive human sensor model in sensor networks

• Published in: 2005 7th International Conference on Information Fusion (FUSION) : Philadelphia, PA, 25-28 July, 2005 Vol. 1; p. 8 pp.
• Main Authors: Kaupp, T., Makarenko, A., Ramos, F., Upcroft, B., Williams, S., Durrant-Whyte, H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Adaptation model; Calibration; data fusion; human-network interaction; Humans; Intelligent networks; Robot sensing systems; Robotics and automation; Sensor fusion; sensor model adaptation; Sensor network; Sensor phenomena and characterization; State estimation; Tin; user modeling
• ISBN: 9780780392861; 0780392868
• DOI: 10.1109/ICIF.2005.1591929

This paper presents the design of a probabilistic model of human perception as an integral part of a decentralized data fusion system. The system consists of a team of human operators and robotic platforms, together forming a heterogeneous sensor network. Human operators are regarded as information sources submitting raw observations. The observations are converted into a probabilistic representation suitable for fusion with the system's belief. The conversion is performed by a human sensor model (HSM). The initial HSM is built offline based on an average of multiple human subjects conducting a calibration experiment. Since individual human operators may vary in their performance, an online adaptation of the HSM is required. The network estimate is used for adaptation because the true feature state is unknown at runtime. Results of an outdoor calibration experiment using range and bearing observations are presented. Simulations show the feasibility of efficient online adaptation.