Alaybeyoglu A.Erciyes K.Kantarci A.2024-07-222024-07-22201317438225http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/17548Accurate tracking of a target is imperative in military as well as civil applications. In this study, we propose a distributed cone based tracking algorithm for a target that can move with highly dynamic kinematics along linear and nonlinear trajectories. The algorithm provides wakening of a group of nodes in a cone shaped region along the trajectory of the target and particle filtering is used in the prediction of the next state of the target to decrease the target missing ratios. Algorithm used is adaptive in that the shape of the cone is determined dynamically in accordance with the target kinematics. We compared our algorithm with traditional tracking approaches, a recent tracking algorithm (Semi-Dynamic Clustering (SDC)) and other tracking algorithms that we have previously proposed. Simulation results show that, in terms of target missing ratios, our algorithm is superior to all of these algorithms. Secondly, lower target missing ratios lead to less frequent execution of recovery mechanisms which in turn results in lower energy consumptions. © 2013 Inderscience Enterprises Ltd.EnglishKalman filtersKinematicsMilitary applicationsMonte Carlo methodsTarget trackingWireless sensor networksDistributed target trackingDistributed trackingDynamic clusteringExtended Kalman filteringParticle FilteringRecovery mechanismsTracking algorithmTracking approachesClustering algorithmsArticle10.1504/IJAHUC.2013.052348