TY - GEN
T1 - Information-driven sensor planning
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
AU - Cochran, Douglas
AU - Hero, Alfred O.
PY - 2013
Y1 - 2013
N2 - Many adaptive sensing and sensor management strategies seek to determine a sequence of sensor actions that successively optimizes an objective function. Frequently the goal is to adjust a sensor to best estimate a partially observed state variable, for example, the objective function may be the final mean-squared state estimation error. Information-driven sensor planning strategies adopt an objective function that measures the accumulation of information as defined by a suitable metric, such as Fisher information, Bhattacharyya affinity, or Kullback-Leibler divergence. These information measures are defined on the space of probability distributions of data acquired by the sensor, and there is a distribution in this space corresponding to each sensor configuration. Hence, sensor planning can be posed as a problem of optimally navigating over a statistical manifold of probability distributions. This information-geometric perspective presents new insights into adaptive sensing and sensor management.
AB - Many adaptive sensing and sensor management strategies seek to determine a sequence of sensor actions that successively optimizes an objective function. Frequently the goal is to adjust a sensor to best estimate a partially observed state variable, for example, the objective function may be the final mean-squared state estimation error. Information-driven sensor planning strategies adopt an objective function that measures the accumulation of information as defined by a suitable metric, such as Fisher information, Bhattacharyya affinity, or Kullback-Leibler divergence. These information measures are defined on the space of probability distributions of data acquired by the sensor, and there is a distribution in this space corresponding to each sensor configuration. Hence, sensor planning can be posed as a problem of optimally navigating over a statistical manifold of probability distributions. This information-geometric perspective presents new insights into adaptive sensing and sensor management.
KW - Adaptive sensing
KW - Hellinger distance
KW - Information geometry
KW - Multinomial class of distributions
KW - Sensor management
UR - http://www.scopus.com/inward/record.url?scp=84897699169&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897699169&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6737074
DO - 10.1109/GlobalSIP.2013.6737074
M3 - Conference contribution
AN - SCOPUS:84897699169
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 1049
EP - 1052
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Y2 - 3 December 2013 through 5 December 2013
ER -