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• W2072196613 abstract "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." @default.
• W2072196613 created "2016-06-24" @default.
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• W2072196613 date "2013-12-01" @default.
• W2072196613 modified "2023-09-27" @default.
• W2072196613 title "Information-driven sensor planning: Navigating a statistical manifold" @default.
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• W2072196613 doi "https://doi.org/10.1109/globalsip.2013.6737074" @default.
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