You’re evaluating a pair of sensors for use in a new data fusion system. You perform experiments and build a joint probability distribution of their outputs under various known conditions. You observe strong dependencies between the sensors’ outputs, even when the true state of the environment is controlled and known. Based on this insight, which of the following is the most appropriate recommendation, and why?

Question

Answer

Recommend skipping data fusion entirely, because dependency in sensor readings violates the assumption of conditional independence.

Answer

Recommend sensor-level fusion, because strong dependencies between sensors make it easier to classify objects when sensors process their own data independently.

Answer

Recommend selecting different sensors that produce uncorrelated data, because dependent sensors cannot be used effectively in fusion.

Answer

Recommend central-level fusion, because dependent sensor outputs are best handled when raw data is combined and processed centrally to exploit their relationships.

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You’re evaluating a pair of sensors for use in a new data fusion system. You per...

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