A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks With Arbitrary Topology

Koutrouvelis, Andreas I.; Sherson, Thomas; Heusdens, Richard; Hendriks, Richard C.

doi:10.1109/taslp.2018.2829405

Cited by 34 publications

(44 citation statements)

References 50 publications

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“…Condition (39) states that the second-order moment of the gradient noise process should get smaller for better estimates, since it is bounded by the squared-norm of the iterate. Conditions (37) and (38) state that the gradient noises across the agents are uncorrelated and second-order circular.…”

Section: A Modeling Conditionsmentioning

confidence: 99%

“…For the partial covariance F •R x to converge to the true covariance R x in (75), we need to set ν = N −1 in order to have F = 1 N 1 N . Note that, two main classes of distributed beamforming appear in the literature [37]. In the first class, which is considered here, the covariance matrix is approximated to form distributed implementations [37]- [40] leading to sub-optimal beamformers.…”

Section: Distributed Linearly Constrained Minimum Variance (Lcmv)mentioning

confidence: 99%

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Adaptation and Learning Over Networks Under Subspace Constraints—Part I: Stability Analysis

Nassif

Vlaski

Sayed

2020

IEEE Trans. Signal Process.

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This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus optimization as a special case, and allows for more general task relatedness models such as smoothness. While such formulations can be solved via projected gradient descent, the resulting algorithm is not distributed. Starting from the centralized solution, we propose an iterative and distributed implementation of the projection step, which runs in parallel with the stochastic gradient descent update. We establish in this Part I of the work that, for small step-sizes µ, the proposed distributed adaptive strategy leads to small estimation errors on the order of µ. We examine in the accompanying Part II [2] the steady-state performance. The results will reveal explicitly the influence of the gradient noise, data characteristics, and subspace constraints, on the network performance. The results will also show that in the small step-size regime, the iterates generated by the distributed algorithm achieve the centralized steady-state performance.where J k (·) is the cost function at agent k, N is the number of agents in the network, and w ∈ C L is the global parameter vector, which all agents need to agree upon-see Fig. 1 (middle). Each agent seeks to estimate w o through local computations and communications among neighboring agents without the need to know any of the costs besides their own. Among many useful strategies that have been proposed in the literature [3]-[10], diffusion strategies [3]-[5] are particularly attractive since they are scalable, robust, and enable continuous learning and adaptation in response to drifts in the location of the minimizer. However, there exist many network applications that require more complex models and flexible algorithms than consensus implementations since their agents may involve the need to estimate and track multiple distinct, though related, objectives. For instance, in distributed power system state estimation, the local state vectors to be estimated at neighboring control centers may overlap partially since the areas in a power system are interconnected [11], [12]. Likewise, in monitoring applications, agents need to track the movement of multiple correlated targets and to exploit the correlation profile in the data for enhanced accuracy [13], [14]. Problems of this kind, where nodes need to infer multiple, though related, parameter vectors, are referred to as multitask problems. Existing strategies to address multitask problems generally exploit prior knowledge on how the tasks across the network relate to each other [11]-[29]. For example, one way to model relationships among tasks is to formulate convex optimization problems with appropriate co-regularizers between neighboring agents [13], [16]-[19]. Graph spectral regularization can also be used in order t...

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Section: A Modeling Conditionsmentioning

confidence: 99%

Section: Distributed Linearly Constrained Minimum Variance (Lcmv)mentioning

confidence: 99%

Adaptation and Learning Over Networks Under Subspace Constraints—Part I: Stability Analysis

Nassif

Vlaski

Sayed

2020

IEEE Trans. Signal Process.

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“…The elements of E, i.e., e k for any k = 1, · · · , K and e, are assumed to be for simplicity independent and identically distributed (i.i.d) Gaussian variables so that the covariance matrices R e k = E[e k e H k ] and R e = E[ee H ] are diagonal. In this case we can directly impose the effects of the uncertainties to all the matrices associated with f k and g in (14). By assuming that the channel errors are uncorrelated with the channels so that E[e k ⊙ g] = 0, E[e ⊙ f k ] = 0, E[e ⊙ g] = 0 and E[e k ⊙ f k ] = 0, then we can use an additive Frobenius norm matrix perturbation [29], which results in…”

Section: System Model and Problem Statementmentioning

confidence: 99%

Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

Ruan

Lamare

2019

IEEE Trans. Signal Process.

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In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and crosscorrelation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-tointerference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.

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“…The identifiability condition in (12) is not sufficient for guaranting unique identifiability [36]. Specifically, for any arbitary non-singular matrix T ∈ C r×r , we have P y (A, P, P v ) = P y (AT −1 , TPT H , P v ) and, therefore [34] F (P y , A, P,…”

Section: Confirmatory Factor Analysismentioning

confidence: 99%

Robust Joint Estimation of Multimicrophone Signal Model Parameters

Koutrouvelis

Hendriks

Heusdens

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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One of the biggest challenges in multi-microphone applications is the estimation of the parameters of the signal model such as the power spectral densities (PSDs) of the sources, the early (relative) acoustic transfer functions of the sources with respect to the microphones, the PSD of late reverberation, and the PSDs of microphone-self noise. Typically, the existing methods estimate subsets of the aforementioned parameters and assume some of the other parameters to be known a priori. This may result in inconsistencies and inaccurately estimated parameters and potential performance degradation in the applications using these estimated parameters. So far, there is no method to jointly estimate all the aforementioned parameters. In this paper, we propose a robust method for jointly estimating all the aforementioned parameters using confirmatory factor analysis. The estimation accuracy of the signal-model parameters thus obtained outperforms existing methods in most cases. We experimentally show significant performance gains in several multi-microphone applications over state-of-the-art methods.

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A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks With Arbitrary Topology

Cited by 34 publications

References 50 publications

Adaptation and Learning Over Networks Under Subspace Constraints—Part I: Stability Analysis

Adaptation and Learning Over Networks Under Subspace Constraints—Part I: Stability Analysis

Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

Robust Joint Estimation of Multimicrophone Signal Model Parameters

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