Detection Theory - Detection Theory We will deal with a...

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Detection Theory We will deal with a binary source, but the theory can be extended to any m-ary source. Example: Binary Communication Example: Radar noise N information source 0 1 receiver R {0,1} D noise N airplane present receiver R {,} D pa abscent probabilistic transition mechanism impluse radio
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Formal Model Source 01 The source generates a binary output, called a hypothesis,.either or HH 00 11 Pr{ is true} Pr{ is true} pH = = and are called a priori probabilities In a data communications problem, it is common to have = However in general, we may not know a priori probabilities pp source decision rule R {,} D HH 1 H probabilistic transition mechanism 0 H
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Probabilistic Transition Mechanism Our probabilistic transition mechanism knows which hypothesis is true Based on this knowledge, it generates the observation variable , which is a point in the observation space Z R | ( ) are called hypothesis-dependent conditional probabilities j RH fr Frequently the observation space may be multi-dimensional In that case, we use and in place of and .
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This note was uploaded on 11/23/2010 for the course EE EE528 taught by Professor Majungsoo during the Spring '10 term at Korea Advanced Institute of Science and Technology.

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Detection Theory - Detection Theory We will deal with a...

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