Optimal bi-level quantization of i.i.d. sensor observations for binary hypothesis testing

@article{Zhang2002OptimalBQ,
  title={Optimal bi-level quantization of i.i.d. sensor observations for binary hypothesis testing},
  author={Qian Zhang and Pramod K. Varshney and Richard D. Wesel},
  journal={IEEE Trans. Information Theory},
  year={2002},
  volume={48},
  pages={2105-2111}
}
We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihood-ratio quantizers with threshold are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known to be a -out-ofrule with threshold . For the Bayesian detection problem, we show that given , the probability of error is a quasiconvex function of and has a single minimum that is achieved by the… CONTINUE READING

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