In practical problems of signal detection, it is quite common that the underlying noise distribution is not Gaussian and may vary in a wide range from light- to heavy-tailed forms. To design a robust fusion rule for distributed detection in wireless sensor networks, an asymptotic maximin approach is used by introducing weak signals in the canonical parallel fusion model. Explicit formulas for the detection and false alarm probabilities are derived. The analytic results are written out for the classes of nondegenerate, with a bounded variance and contaminated Gaussian noise distributions. Numerical and simulation results are obtained to justify robustness and asymptotic characteristics of the proposed fusion rule.
['Jin-Tae Park', 'Georgy Shevlyakov', 'Kiseon Kim']
Robust Distributed Detection with Total Power Constraint in Large Wireless Sensor Networks