首页> 外文会议>Proceedings of the 2016 IEEE-APS Topical Conference on Antennas and Propagationin Wireless Communications >Multiple signal classification algorithm compensated by Extended Kalman Particle Filtering for Wi-Fi through wall multi-target tracking
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Multiple signal classification algorithm compensated by Extended Kalman Particle Filtering for Wi-Fi through wall multi-target tracking

机译:通过墙多目标跟踪,采用扩展卡尔曼粒子滤波补偿Wi-Fi的多信号分类算法

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In this work, a multiple-target tracking problem for a Wi-Fi through wall system is formulated and a new Direction Of Arrival (DOA) angle estimation technique is investigated to solve the tracking problem in the presence of clutter. The DOA estimation from objects behind walls is investigated utilizing the MUltiple SIgnal Classification (MUSIC) algorithm compensated by Extended Kalman Particle Filtering (EKPF) technique for the first time. Simulation results show that the stand-alone MUSIC algorithm fails to identify two distinct objects having close DOAs and fails to track targets when they are moving close to each other. The results also reveal that the EKPF algorithm in conjunction with MIMO nulling technique correctly identifies close and overshadowed moving objects and improves the tracking success rate.
机译:在这项工作中,提出了一种穿墙系统对Wi-Fi的多目标跟踪问题,并研究了一种新的到达方向(DOA)角度估计技术,以解决杂乱情况下的跟踪问题。首次利用扩展卡尔曼粒子滤波(EKPF)技术补偿的多信号分类(MUSIC)算法研究了墙后物体的DOA估计。仿真结果表明,独立的MUSIC算法无法识别出具有接近DOA的两个不同对象,并且当它们彼此靠近时无法跟踪目标。结果还表明,EKPF算法与MIMO归零技术相结合,可以正确识别出近处和阴影中的运动物体,并提高了跟踪成功率。

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