TY - GEN
T1 - Dimensionality reduction design for distributed estimation in certain inhomogeneous scenarios
AU - Fang, Jun
AU - Li, Hongbin
PY - 2011
Y1 - 2011
N2 - We consider distributed estimation of a deterministic vector parameter from noisy sensor observations in a wireless sensor network (WSN). To meet stringent power and bandwidth budgets inherent in WSNs, local data dimensionality reduction is performed at each sensor to reduce the number of messages sent to a fusion center (FC). The problem of interest is to jointly design the compression matrices associated with those sensors, aiming at minimizing the estimation error at the FC. Such a dimensionality reduction problem is investigated in this paper. Specifically, we study an inhomogeneous environment where the noise covariance matrices across the sensors have the same correlation structure but with different scaling factors. Given a total number of messages sent to the FC, theoretical lower bounds on the estimation error of any compression strategy are derived. Compression strategies are developed to approach or even attain the corresponding theoretical lower bounds. Performance analysis and simulations are carried out to illustrate the optimality and effectiveness of the proposed compression strategies.
AB - We consider distributed estimation of a deterministic vector parameter from noisy sensor observations in a wireless sensor network (WSN). To meet stringent power and bandwidth budgets inherent in WSNs, local data dimensionality reduction is performed at each sensor to reduce the number of messages sent to a fusion center (FC). The problem of interest is to jointly design the compression matrices associated with those sensors, aiming at minimizing the estimation error at the FC. Such a dimensionality reduction problem is investigated in this paper. Specifically, we study an inhomogeneous environment where the noise covariance matrices across the sensors have the same correlation structure but with different scaling factors. Given a total number of messages sent to the FC, theoretical lower bounds on the estimation error of any compression strategy are derived. Compression strategies are developed to approach or even attain the corresponding theoretical lower bounds. Performance analysis and simulations are carried out to illustrate the optimality and effectiveness of the proposed compression strategies.
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U2 - 10.1109/ICDSP.2011.6004880
DO - 10.1109/ICDSP.2011.6004880
M3 - Conference contribution
AN - SCOPUS:80053162392
SN - 9781457702747
T3 - 17th DSP 2011 International Conference on Digital Signal Processing, Proceedings
BT - 17th DSP 2011 International Conference on Digital Signal Processing, Proceedings
T2 - 17th International Conference on Digital Signal Processing, DSP 2011
Y2 - 6 July 2011 through 8 July 2011
ER -