NDLI: Recursive LMMSE centralized fusion with recombination of multi-radar measurements

Content Provider	IEEE Xplore Digital Library
Author	Zhansheng Duan Yimin Wang Li, X.R.
Copyright Year	2011
Description	Author affiliation: Center for Information Engineering Science Research, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China (Zhansheng Duan; Yimin Wang) \|\| Department of Electrical Engineering, University of New Orleans, New Orleans, LA 70148 (Li, X.R.)
Abstract	For target tracking with radar measurements, recursive LMMSE (Linear Minimum Mean Squared Error) filtering outperforms the popular measurement conversion based Kalman filters, which have some serious drawbacks in terms of both estimation accuracy and credibility. The existing recursive LMMSE with measurements from a single radar is first extended to the multi-radar case. It is then shown that recombination plays an important role in performance improvement for recursive LMMSE centralized fusion using multiple radars. Here, “recombination” means shuffling all scalar measurements from the multiple radars, dimension by dimension. This differs from the case of centralized fusion with linear measurements from multiple sensors. Numerical simulation examples are provided to illustrate the use of recombination in recursive LMMSE centralized fusion for the nonlinear radar measurements.
Starting Page	1
Ending Page	8
File Size	300411
Page Count	8
File Format	PDF
ISBN	9781457702679
e-ISBN	9780982443828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Intl Society of Info Fusion
Subject Keyword	Radar measurements Sensors Coordinate measuring machines Estimation Noise Target tracking target tracking Centralized fusion recombination recursive LMMSE filtering nonlinear filtering radar measurements
Content Type	Text
Resource Type	Article

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