Quantizer Design for Generalized Locally Optimum Detectors in Wireless Sensor Networks

Abstract

We tackle distributed detection of a non-cooperative (i.e., whose emitted power is unknown) target with a wireless sensor network. When the target is present, sensors observe an (unknown) randomly fluctuating signal with attenuation depending on the distance between the sensor and the (unknown) target positions, embedded in Gaussian noise. The fusion center receives (only) sensor decisions through error-prone binary symmetric channels and is in charge of performing a more-accurate global inference. The resulting test is one-sided with nuisance parameters (i.e., the target position) present only under the hypothesis $\pmb {\mathcal {H}}_{{1}}$ . To reduce the complexity of generalized likelihood ratio test, a generalized locally optimum detection test (based on Davies’ framework) is investigated and a corresponding sensor threshold optimization (based on a semi-theoretical criterion) is developed and verified through simulations.