DocumentCode :
1365055
Title :
Globally Optimized Power Allocation in Multiple Sensor Fusion for Linear and Nonlinear Networks
Author :
Rashid, Umar ; Tuan, Hoang Duong ; Apkarian, Pierre ; Kha, Ha Hoang
Author_Institution :
Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
Volume :
60
Issue :
2
fYear :
2012
Firstpage :
903
Lastpage :
915
Abstract :
The present paper is concerned with a sensor network, where each sensor is modeled by either a linear or nonlinear sensing system. These sensors team up in observing either static or dynamic random targets and transmit their observations through noisy communication channels to a fusion center (FC) for locating/tracking the targets. Physically, the network is limited by energy resource. According to the available sum power budget, we develop a novel technique for power allocation to the sensor nodes that enables the FC produce the best linear estimate in terms of the mean square error (MSE). Regardless of whether the sensor measurements are linear or nonlinear, the targets are scalar or vectors, static or dynamic, the corresponding optimization problems are shown to be semidefinite programs (SDPs) of tractable optimization and thus are globally and efficiently solved by any existing SDP solver. In other words, new tractably computational algorithms of distributed Bayes filtering are derived with full multisensor diversity achieved. Intensive simulation shows that these algorithms clearly outperform previously known algorithms.
Keywords :
Bayes methods; mathematical programming; mean square error methods; sensor fusion; target tracking; MSE; distributed Bayes filtering; dynamic random target; fusion center; mean square error; multisensor diversity; noisy communication channel; nonlinear sensing system; power allocation; semidefinite program; sensor fusion; sensor measurement; sensor network; sensor node; static random target; target location; target tracking; Approximation methods; Covariance matrix; Filtering; Resource management; Sensors; Target tracking; Tin; Bayes filtering; data fusion; linear and nonlinear sensor network; linear fractional transformation (LFT); power allocation; semidefinite programming (SDP); unscented transformations;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2011.2174230
Filename :
6064911
Link To Document :
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