Robust distributed detection over adaptive diffusion networks

Abstract

Diffusion adaptation techniques based on the least-meansquares criterion have been proposed for distributed detection of a signal in Gaussian-distributed noise, forgoing the need for a fusion center. However, least-mean-squares solutions are generally non-robust against impulsive noise. In this work, we combine nonlinear filtering with diffusion adaptation and propose a strategy for distributed detection in the presence of impulsive noise. The superiority of the algorithm is validated experimentally.

Nonlinear diffusion adaptation with bounded transmission over distributed networks

“…Convergence in the Mean For the error estimatew w w i to converge to zero, the error recursions of (13) and (14) should be stable as i → ∞. We compare the stabilities of the two diffusion adaptations in the mean sense.…”

“…, M}, according to (A1). Let P P P i (13), the error recursion of (12) for the nonlinear case can be simplified as…”

“…In [13], an error nonlinearity in each node's adaptation was applied due to impulsive measurement noise. However, we are interested in nonlinearity of diffusion updates among the nodes, which occurs when local intermediate estimates are diffused with those of neighboring nodes through bounded transmissions.…”

Classification techniques for adaptive distributed networks and aeronautical structures.

“…Detection using adaptive networks has received considerable attention due to its energy saving capability, less communication resource requirements, robustness to node and link failure, scalability and tracking performance [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], employing different detection strategies depending on the application. For instance, several works focus on distributed Neyman-Pearson (NP) detectors [36], an approach appropriate, for example, to detect the presence of a target in a radar, since this detector maximizes the probability of detection of an event given a maximum desirable probability of false alarm.…”

“…This thesis proposes an ML detector over a distributed network, which decides between two concurrent hypotheses about the environment state -in EMBRAER's context, for instance, it could be imagined as a detector that decides if an AE source is due to one or another damage in the structure; or even decides between actual damage or noise. The decision algorithm is based on the estimation technique diffusion LMS [19,44], which was applied in [23][24][25][26][27][28][29][30][31] to design distributed NP detectors. These works use a diffusion learning process among the nodes [45,46], but there are several others relying on the consensus technique [32][33][34][35].…”