Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.Comment: 12 pages, 9 figure

A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises

This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over a sensor network with a given topology. The system presents the following main features: (i) random parameter matrices in both the state and observation equations are considered; and (ii) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are obtained at each sensor node by processing available output measurements not only from the sensor itself but also from its neighboring sensors according to the network topology. At the second stage, noting that at each sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed estimators, which is measured by the error covariance matrices, is examined by a numerical simulation example where a four-sensor network is considered. The example illustrates the applicability of the proposed results to a linear networked system with state-dependent multiplicative noise and different network-induced stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements and multiplicative observation noises are considered as particular cases of the proposed observation model.This research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014- 52291-P, MTM2017-84199-P)

Distributed adaptive node-specific signal estimation in heterogeneous and mixed-topology wireless sensor networks

© 2015 Elsevier B.V. All rights reserved. A wireless sensor network (WSN) is considered where each node estimates a number of node-specific desired signals by means of the distributed adaptive node-specific signal estimation (DANSE) algorithm. It is assumed that the topology of the WSN is constructed based on one of the two approaches, either a top-down approach where the WSN is composed of heterogeneous nodes, or a bottom-up approach where the nodes are not necessarily heterogeneous. In the top-down approach, nodes with the largest energy budgets are designated as cluster heads and the remaining nodes form clusters around these nodes. In the bottom-up approach, an ad hoc WSN is partitioned into a set of smaller substructures consisting of non-overlapping cliques that are arranged in a tree topology. These two approaches are shown to be conceptually equivalent, in that the same building blocks constitute both envisaged topologies, and the functionality of the DANSE algorithm is extended to such topologies. In using the DANSE algorithm in such topologies, the WSN converges to the same solution as if all nodes had access to all of the sensor signal observations, and provides faster convergence when compared to DANSE in a single tree topology with only a slight increase in per-node energy usage.status: publishe

Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks

The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm. The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT). Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs. In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation. First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced. A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented. Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition. In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted. Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations. Then, sparse non-negative blind energy separation is realized using multiplicative updates. Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation. Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner. In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source. This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising. Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed. A robust and regularized $t_{\nu}M$-estimation of the covariance matrix of the mixed energies is employed. This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN. The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement. The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns. In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation. Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented. Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem. In this way, the strongest sparse right singular vectors using a robust $\ell_1$-norm and stability selection are the set of basis vectors that describe the input data efficiently. Active/non-active source classification is achieved based on a robust Mahalanobis classifier. For this, a robust $M$-estimator of the covariance matrix in the Mahalanobis distance is utilized. Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach. Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction. With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals. A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array. The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source. The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source. Reliability is defined with respect to the median across frequencies. All source-specific frequency bands that contribute to correct estimated angles are selected. A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle. Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging