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Cooperative Multiple Target Nodes Localization Using TOA in Mixed LOS/NLOS Environments

机译:在混合LOS / NLOS环境中使用TOA的协同多个目标节点本地化

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In this paper, the time-of-arrival based cooperative localization problem under mixed line-of-sighton-line-of-sight (LOS/NLOS) conditions is addressed. Under the conditions of unknown statistics of NLOS errors and unknown path status, we formulate a non-convex robust weighted least squares (RWLS) problem to locate the multiple stationary target nodes. In this problem, we introduce some "balancing parameters" to alleviate the possible improper upper bounds problem of the existing robust localization methods, and jointly estimate the target node positions and the balancing parameters. With the aid of the semidefinite relaxation technique, the RWLS problem is transformed into a convex mixed semidefinite and second order cone programming problem, which can be solved efficiently. The idea is then extended to the moving target nodes localization problem, where the velocity is assumed to be fixed within a short time period. Both simulated and real experimental data are used to verify the performance of the proposed method. The results show that the proposed method works well for both the sparse and dense NLOS environments, and also confirm the superior performance of the proposed method over the existing methods.
机译:在本文中,解决了混合视线/非视线(LOS / NLOS)条件下的到达时间基于的合作定位问题。在NLOS错误和未知路径状态的未知统计数据的条件下,我们制定非凸起稳健的加权最小二乘(RWL)问题以定位多个固定目标节点。在这个问题中,我们介绍了一些“平衡参数”来缓解现有的强大本地化方法的可能不当的上限问题,并联合估计目标节点位置和平衡参数。借助于半纤维弛豫技术,RWLS问题被转变为凸混合的半纤维和二阶锥编程问题,可以有效地解决。然后将该想法扩展到移动目标节点定位问题,其中假设速度在短时间内固定。模拟和实际实验数据都用于验证所提出的方法的性能。结果表明,该方法适用于稀疏和密集的NLOS环境,还对现有方法确认所提出的方法的卓越性能。

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