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Distributed estimation and tracking for radio environment mapping
2014
2014 American Control Conference
We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization
doi:10.1109/acc.2014.6859200
dblp:conf/amcc/KongZG14
fatcat:ufmeo3ksuvh27m56qd35l7o74e