### ASA 127th Meeting M.I.T. 1994 June 6-10

## 4pUW17. The application of the Bayesian fusion detection criterion to the
case of two disparate signals.

**Teenia T. Perry
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*Naval Res. Lab., Code 7175, Stennis Space Center, MS 39529-5004
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**William M. Sanders
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*RSMAS, University of Miami, Miami, FL 33149
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A Bayesian fusion criterion is described and applied to estimate
improvements in detection range for two collocated sensors of disparate
signals. The amplitude detection threshold selected for each sensor is
expressed in terms of a likelihood threshold. The likelihood function is the
ratio of the area under the probability density function beyond a threshold for
a given signal to that for noise. The Bayesian criterion allows tying the
likelihood threshold for one sensor to that for the other. When one can be set
high, the other can be set low, thereby optimizing the use of the two sensor
types. The assumption of Gaussian signal and noise probability density
functions enables solving for the combined probability of detection versus
range using an error function. An example is given for two signal types where
each is subject to a different spreading loss. Estimates indicate that when the
two individual detection ranges are comparable, the improvement in detection
range through the application of a Bayesian criterion when compared to a simple
``or'' criterion (each sensor used separately with fixed nonrelated likelihood
thresholds) can be as much as 30%. [Work supported by ONR.]