Distributed Estimation Using Reduced-Dimensionality Sensor Observations

Schizas, Ioannis D.; Giannakis, Georgios B.; Luo, Zhi-Quan

doi:10.1109/tsp.2007.895987

Cited by 202 publications

(221 citation statements)

References 11 publications

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“…The sensor nodes collect observations of a physical phenomenon and collaborate with each other to perform a certain signal processing task, e.g., localization, detection or estimation of certain signals or parameters. Some approaches require a socalled fusion center (e.g., [1]- [6]) that gathers all the sensor signals, whereas other algorithms are distributed such that all processing happens inside the network (e.g., [7]- [23]). The latter is usually preferred, especially when it is scalable in terms of communication bandwidth and processing power.…”

Section: A Distributed Signal Estimation In Wireless Sensor Networkmentioning

confidence: 99%

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Bertrand

Moonen

2013

IEEE Trans. Signal Process.

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Abstract-Linearly constrained minimum variance (LCMV) beamforming is a popular spatial filtering technique for signal estimation or signal enhancement in many different fields. We consider distributed LCMV (D-LCMV) beamforming in wireless sensor networks (WSNs) with either a fully connected or a tree topology. In the D-LCMV beamformer algorithm, each node fuses its multiple sensor signals into a single-channel signal of which observations are then transmitted to other nodes. We envisage an adaptive/time-recursive implementation where each node adapts its local LCMV beamformer to changes in the local sensor signal statistics, as well as to changes in the statistics of the wirelessly received signals. Although the per-node signal transmission and computational power is greatly reduced compared to a centralized realization, we show that it is possible for each node to generate the centralized LCMV beamformer output as if it had access to all sensor signals in the entire network, without an explicit computation of the network-wide sensor signal covariance matrix. We provide sufficient conditions for convergence and optimality of the D-LCMV beamformer. The theoretical results are validated by means of Monte-Carlo simulations, which demonstrate the performance of the D-LCMV beamformer.

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Section: A Distributed Signal Estimation In Wireless Sensor Networkmentioning

confidence: 99%

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Bertrand

Moonen

2013

IEEE Trans. Signal Process.

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“…According to [9], for i.i.d. local observations an upper bound for the MSE, when the messages are transmitted over a memoryless binary symmetric channel is given by the following lemma: Lemma 6: [9] If the bit error rates from node k is , then the MSE achieved by the fusion center based on the decoded messages {m 1 ,..., m n } is upper bounded by (19) where and p 0 > 0 satisfies the following condition:…”

Section: ) Quantized Local Processingmentioning

confidence: 99%

“…where is the L 2 -norm of the power vector P = [P 1 ,..., P n ] T , ε 2 is the desired MSE threshold at the fusion center and the MSE is as given by (19). The optimal number of bits to quantize the observations at node k, that is given by the solution to (21), are characterized in the following lemma.…”

Section: ) Quantized Local Processingmentioning

confidence: 99%

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

Wimalajeewa¹,

Jayaweera²,

Mosquera³

2009

Sensor and Data Fusion

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“…In many multi-node estimation frameworks the measurement data is fused, possibly through a fusion center, to estimate a common parameter or signal assumed to be the same for each node (e.g. [2][3][4][5][6]). This can be viewed as a special case of the more general problem where each node in the network estimates a different node-specific signal.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike other compression schemes for multi-dimensional sensor data (e.g. [4][5][6]), the algorithm does not need prior knowledge of the intra-and inter-sensor cross-correlation structure of the network. Nodes estimate and re-estimate all necessary statistics on the compressed data during operation.…”

Section: Introductionmentioning

confidence: 99%

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

Bertrand

Moonen

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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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.

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Distributed Estimation Using Reduced-Dimensionality Sensor Observations

Cited by 202 publications

References 11 publications

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

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

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