Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7228
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dc.contributor.authorSatapathi, G.S.
dc.contributor.authorSrihari, P.
dc.date.accessioned2020-03-30T09:58:40Z-
dc.date.available2020-03-30T09:58:40Z-
dc.date.issued2017
dc.identifier.citation2016 IEEE Annual India Conference, INDICON 2016, 2017, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7228-
dc.description.abstractThis paper proposes a novel data association approach based on fuzzy relational clustering for multi target tracking in the presence of electronic counter measures (ECM). Likelihood values and similarity index are calculated for each observation obtained from radar. Expectation maximization technique is applied to obtain possibility association matrix. Simulation results demonstrate that, proposed method performs better, when compared to conventional joint probability association (JPDA) and fuzzy clustering (FCM) approaches in terms of position and velocity root mean square error (RMSE). Further, current approach yielded average reduction of 50.5% and 35.5% for position and velocity RMSE values respectively in case of linear crossing targets. � 2016 IEEE.en_US
dc.titleAll neighbor fuzzy relational data association for multitarget tracking in the presence of ECMen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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