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Sequential optimal positioning of mobile sensors using mutual information
Author(s) -
Schmidt Kathleen,
Smith Ralph C.,
Hite Jason,
Mattingly John,
Azmy Yousry,
Rajan Deepak,
Goldhahn Ryan
Publication year - 2019
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11431
Subject(s) - computer science , mutual information , a priori and a posteriori , entropy (arrow of time) , position (finance) , flexibility (engineering) , set (abstract data type) , trajectory , data mining , real time computing , artificial intelligence , mathematics , statistics , philosophy , physics , epistemology , finance , quantum mechanics , astronomy , economics , programming language
Source localization, such as detecting a nuclear source in an urban area or ascertaining the origin of a chemical plume, is generally regarded as a well‐documented inverse problem; however, optimally placing sensors to collect data for such problems is a more challenging task. In particular, optimal sensor placement—that is, measurement locations resulting in the least uncertainty in the estimated source parameters—depends on the location of the source, which is typically unknown a priori . Mobile sensors are advantageous because they have the flexibility to adapt to any given source position. While most mobile sensor strategies designate a trajectory for sensor movement, we instead employ mutual information, based on Shannon entropy, to choose the next measurement location from a discrete set of design conditions.