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In this paper,we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network.Since a group of targets moves collectively and is restricted within a limited region,it is not worth consuming scarce resources of sensors in computing the trajectory of each single target.Hence,in this paper,the problem is modeled as tracking a geographical continuous region covered by all targets.A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period.Based on the locations of sensors and the azimuthal angle of arrival(AOA) information,the estimated region covering all the group members is obtained.Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group.Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error,which is between 10%and 40%of the hull algorithm,with a similar density of sensors.And when the density of sensors increases,the localization accuracy of the proposed algorithm improves dramatically.
In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves limited and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target .ence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. of sensors and the azimuthal angle of arrival (AOA) information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors .An d when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.