An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
Diffusion LMS-based distributed detection over adaptive networks
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Diffusion least-mean squares with adaptive combiners: formulation and performance analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Distributed estimation over complex networks
Information Sciences: an International Journal
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
Collaborative estimation of environmental parameters
IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things
Personal and Ubiquitous Computing
| Hi-index | 35.71 |
We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus, requiring a large amount of energy for communication. Incremental strategies that obtain the global solution have been proposed, but they require the definition of a cycle through the network. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution. We also show how to select the combination weights optimally.