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- W2187362008 abstract "Utilization ofinformation acquired froma sensor networktoimprove thetracking accuracy isoneofthemost important issues insensor network research. Anapproach that consists ofsensor-based filtering algorithms, local processors andglobal processor isemployed todescribe thedistributed fusion problem whenseveral sensors execute surveillance over thecertain area.Forsensortracking systems, eachfiltering algorithm utilized intheReference Cartesian Coordinate System(RCCS)ispresented fortargettracking whenthe radarmeasuresrange,bearing andelevation angleinthe Spherical Coordinate System (SCS). Forlocal processors, each track-to-track fusion algorithm isusedtomergetwotracks representing thesametarget. Thenumberof2-combinations of a setwithN distinct sensors isconsidered forcentral track fusion. Forglobal processor, a Bayesian methodwithSemi- Markov processisemployedto developtheModified Probabilistic NeuralNetwork(MPNN)algorithm tocompute theweights oflocalprocessors. By usinglocalprocessor weights, thesubsequent datafusion isexecuted tocombine the corresponding joint stateestimates. Simulation results are presented comparing theperformance oftheglobal MPNN algorithm withthelocal-level fusion algorithm andwiththe sensor-level filtering algorithm." @default.
- W2187362008 created "2016-06-24" @default.
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- W2187362008 date "2006-01-01" @default.
- W2187362008 modified "2023-09-27" @default.
- W2187362008 title "Integrated Track-to-Track Fusionwith ModifiedProbabilistic NeuralNetwork" @default.
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