Distributed adaptive estimation of correlated node-specific signals in a fully connected sensor network

Abstract

We introduce a distributed adaptive estimation algorithm operating in an ideal fully connected sensor network. The algorithm estimates node-specific signals at each node based on reduced-dimensionality sensor measurements of other nodes in the network. If the nodespecific signals to be estimated are linearly dependent on a common latent process with a low dimension compared to the dimension of the sensor measurements, the algorithm can significantly reduce the required communication bandwidth and still provide the optimal linear estimator at each node as if all sensor measurements were available in every node. Because of its adaptive nature and fast convergence properties, the algorithm is suited for real-time applications in dynamic environments, such as speech enhancement in acoustic sensor networks.

Robust Distributed Noise Reduction in Hearing Aids with External Acoustic Sensor Nodes

“…Furthermore, the effectiveness of relaxation is shown when nodes update their filters simultaneously, as well as the influence of several parameters such as the DFT size used for frequency domain processing, and possible delays within the communication link. The simulations in this paper show the potential of DANSE for noise reduction, as suggested in [13,14], and provide a proof-of-concept for applying the algorithm in cooperative acoustic sensor networks for distributed noise reduction applications, such as hearing aids.…”

“…Furthermore, the dimensions of the correlation matrix to be inverted in formula (4) may grow significantly. However, if each node has its own signal processor unit, this extra communication bandwidth can be reduced and the computation can be distributed by using the distributed adaptive node-specific signal estimation (DANSE) algorithm, as proposed in [13,14]. The DANSE algorithm computes the optimal network wide Wiener filter in a distributed, iterative fashion.…”

“…Define x i k similarly as (14), but now only containing the desired speech components in the considered signals. The update performed by node k is then…”

“…where y[t] denotes the sample of the multi-channel signal y at time t. The forgetting factor λ is chosen close to 1 to obtain long-term estimates that mainly capture the spatial coherence between the microphone signals. In the DANSE K algorithm, however, the statistics of the input signal y k in node k, defined by (14), change whenever a node q / = k updates its filters, since some of the channels in y k are indeed outputs from a filter in node q. Therefore, when node q updates its filters, parts of the estimated correlation matrices R yy,k and R xx,k , ∀ k ∈ {1, .…”

“…Therefore, a multichannel Wiener filter (MWF) approach is considered, since an MWF estimates the clean speech signal without relying on prior knowledge on the microphone positions [12]. In [13,14], a distributed adaptive node-specific signal estimation (DANSE) algorithm is introduced for linear MMSE signal 2 EURASIP Journal on Advances in Signal Processing estimation in a sensor network, which significantly reduces the communication bandwidth while still obtaining the optimal linear estimators, that is, the Wiener filters, as if each node has access to all signals in the network. The term "node-specific" refers to the scenario in which each node acts as a data-sink and estimates a different desired signal.…”