NDLI: Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using WSNs

Content Provider	IEEE Xplore Digital Library
Author	Hao Zhu Schizas, I.D. Giannakis, G.B.
Copyright Year	1991
Abstract	Estimation of nonstationary dynamical processes is of paramount importance in various applications including target tracking and navigation. The goal of this paper is to perform such tasks in a distributed fashion, using data collected at power-limited sensors which either communicate with a fusion center (FC) over noisy links, or, communicate with each other over nonideal channels in an ad hoc setting. In FC-based wireless sensor networks (WSNs) with a prescribed power budget, linear dimensionality reducing operators which account for the sensor-to-FC channel are derived per sensor to minimize the mean-square error (MSE) of Kalman filtered state estimates formed at the FC. Using these operators and state predictions fed back from the FC online, sensors reduce the dimensionality of their local innovation sequences and communicate them to the FC where tracking estimates are corrected. Analytical and numerical results advocate that the novel channel-aware distributed tracker outperforms competing alternatives. In ad hoc WSNs deployed to perform distributed tracking, one sensor broadcasts reduced-dimensionality data per time slot, according to a prespecified transmission order. The dimensionality reducing operators employed by the broadcasting sensor are selected to meet its transmit-power budget, while minimizing the state estimation MSE of the sensor with the lowest receiving SNR. Based on the received reduced-dimensionality data from the broadcasting sensor, every sensor in range performs the MSE optimal tracking. Corroborating distributed target tracking simulations based on distance-only observations illustrate that the novel scheme provides sensors with accurate estimates at affordable communication cost.
Sponsorship	IEEE Signal Processing Society
Starting Page	3193
Ending Page	3207
Page Count	15
File Size	1284679
File Format	PDF
ISSN	1053587X
Volume Number	57
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Wireless sensor networks Target tracking State estimation Broadcasting Navigation Sensor fusion Nonlinear filters Technological innovation Costs target tracking Distributed tracking wireless sensor networks (WSNs) Kalman filtering
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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