A distributed algorithm for robust LCMV beamforming

2016 24th European Signal Processing Conference (EUSIPCO)

Kleijn

2016

Self Cite

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality to form equivalent distributed consensus problems. Such problems can be embedded in sensor network applications via existing solvers such as the alternating direction method of multipliers or the primal dual method of multipliers. We then demonstrate how this approach can be used to solve specific problems including linearly constrained quadratic problems and the classic Gaussian channel capacity maximisation problem in a distributed manner.

Section: Iii-a Example: Constrained Quadratic Programmingmentioning

confidence: 98%

“…it can be shown that problems (5) and (3) are equivalent [16]. As the stationary points of (6) occur when…”

Section: Iii-a Example: Constrained Quadratic Programmingmentioning

confidence: 99%

On the duality of globally constrained separable problems and its application to distributed signal processing

Sherson

2016 24th European Signal Processing Conference (EUSIPCO)

Kleijn

2016

Self Cite

“…In contrast, in [16], an optimal distributed beamformer was proposed for use in cyclic networks by exploiting the structure of estimated CPSDMs to cast LCMP beamforming as distributed consensus. However, for CPSDM estimates based on a large number of frames, the proposed algorithm's communication cost scaled poorly.…”

Section: Distributed Linearly Constrained Beamformersmentioning

confidence: 99%

“…In a similar vein, in [26], a distributed beamformer based on the pseudo-coherence principle was proposed. Similar to [16], the method in [26] can operate in cyclic networks. Furthermore, the authors demonstrated how the algorithm could perform near optimally with only a finite number of iterations, resulting in low transmission complexity.…”

Section: Introductionmentioning

confidence: 99%

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

Koutrouvelis

Sherson

IEEE/ACM Trans. Audio Speech Lang. Process.

et al. 2018

Self Cite

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function (RATF) estimation errors and to target activity detection (TAD) errors. Two variants of the proposed beamformer are presented and evaluated in the context of multi-microphone speech enhancement in a wireless acoustic sensor network, and are compared with other state-ofthe-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors.

“…We construct our distributed optimization problem in a similar manner to the approach in [29]. First thing to notice is that the enhanced signal would not be accessible in a distributed manner unless the locally calculable output, Z m , becomes a side product of the algorithm or an optimization parameter itself.…”

Section: Distributed Optimization With Pdmmmentioning

confidence: 99%

Ad hoc microphone array beamforming using the primal-dual method of multipliers

Tavakoli

Jensen

2016 24th European Signal Processing Conference (EUSIPCO)

et al. 2016

Self Cite

Abstract-In the recent years, there have been an increasing amount of researches aiming at optimal beamforming with ad hoc microphone arrays, mostly fusion-center-based schemes. However, huge computational complexities and communication overheads impede many of these algorithms from being useful in practice. In this paper, we propose a low-footprint optimization approach to reduce the convergence time and overheads for the distributed beamforming problem. We transcribe the pseudocoherence-based beamforming which is insightful for taking into account the nature of speech. We formulate the distributed minimum variance distortionless response beamformer using the primal-dual method of multipliers. Our experiments confirm the fast convergence using the proposed distributed algorithm. It is also shown how a hard limit on the number of iterations affects the performance of the array in noise and interference suppression.Index Terms-Speech enhancement, ad hoc microphone array, distributed beamforming, primal-dual method of multipliers.