The quantized detection algorithm

An algorithm is presented for designing optimum quantizers for signals at two remote sensors that are to be fused at a central site in order to make a detection decision. Fusion rules are selected as candidates according to their ability to approximate the likelihood ratio test, a continuous curve i...

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Bibliographic Details
Main Authors: Moose, P.H., Al-Bassiouni, A.A.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic signal detection
Detection algorithms
Gaussian noise
Quantization
Radar detection
Random variables
Sensor systems
Signal processing algorithms
Statistics
Testing
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Summary:An algorithm is presented for designing optimum quantizers for signals at two remote sensors that are to be fused at a central site in order to make a detection decision. Fusion rules are selected as candidates according to their ability to approximate the likelihood ratio test, a continuous curve in the observation space, with stepwise continuous approximations. The number of steps is determined by N, the number of levels of quantization. Results are presented showing the uniform convergence of the algorithm's performance to that of the likelihood ratio test with increasing N for known signals in Gaussian noise. It is shown that an N of four, or two-bit quantization, performs nearly as well as the likelihood ratio test and is far superior to an N of two, or one-bit quantization, which corresponds to local detection decisions.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1989.266567