We derive linear estimators of stationary random signals based on reduced-dimensionality observations collected at distributed sensors and communicated to a fusion center over wireless links. Dimensionality reduction compresses sensor data to meet low-power and bandwidth constraints, while linearity in compression and estimation are well motivated by the limited computing capabilities wireless sensor networks are envisioned to operate with, and by the desire to estimate random signals from observations with unknown probability density functions. In the absence of fading and fusion center noise (ideal links), we cast this intertwined compression-estimation problem in a canonical correlation analysis framework and derive closed-form mean-square error (MSE) optimal estimators along with coordinate descent suboptimal alternatives that guarantee convergence at least to a stationary point. Likewise, we develop estimators based on reduced-dimensionality sensor observations in the presence of fading and additive noise at the fusion center (nonideal links). Performance analysis and corroborating simulations demonstrate the merits of the novel distributed estimators relative to existing alternatives.
|Original language||English (US)|
|Number of pages||16|
|Journal||IEEE Transactions on Signal Processing|
|State||Published - Aug 2007|
Bibliographical noteFunding Information:
Manuscript received November 6, 2005; revised August 10, 2006. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Javier Garcia-Frias. Prepared through collaborative participation in the Communications and Networks Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The work of Z.-Q. Luo is supported by the U.S. DoD Army, grant number W911NF-05-1-0567. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. Parts of the paper were presented at the Thirty-Ninth Asilomar Conference, Pacific Grove, CA, Oct. 30–November 2, 2005, and at the 2006 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 14–19, 2006.
- Canonical correlation analysis (CCA)
- Distributed compression
- Distributed estimation
- Nonlinear optimization
- Wireless sensor networks (WSNs)