SENSOR CONTROL

摘要

The system/method predicts (204) states of the targets. The sensor receives information regarding a state of a said target (104B) from the plurality of targets obtained by at least one other sensor (102B). A set of probability distributions is generated (402). Each probability distribution represents a setting or settings of at least one control parameter of the sensor. An expected information gain value for each control parameter in the set is calculated (404). Updating (308, 310) of the probability distributions takes place to identify the control parameters that maximise the expected gain value. The set of probability distributions for the sensor and the at least one other sensor are updated using a global objective function. A Monte Carlo Optimisation technique involving immediate sampling and parametric learning is used for the updating of the probability distributions. The sensor is then controlled (206) in accordance with the maximising control parameters.