Large Deviations Performance of Consensus+Innovations Distributed Detection With Non-Gaussian Observations

We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as centralized, with the same optimal asymptotic detection performance, but, below the threshold, distributed detection is suboptimal with respect to centralized detection. We determine this threshold and quantify the performance loss below threshold. Finally, we show the dependence of the threshold and of the performance on the distribution of the observations: the asymptotic performance of distributed detectors over the same random network with different observations' distributions, for example, Gaussian, Laplace, or quantized, may be different, even though the asymptotic performance of the corresponding centralized detectors is the same.