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Particle swarm optimization-based low-complexity three-dimensional UWB localization scheme

机译:基于粒子群优化的低复杂度三维UWB定位方案

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To provide a location-based service (LBS), it is needed to obtain an exact location of communication terminals in sensor networks. Because the signal of global positioning system (GPS) cannot be received indoors, a triangulation-based location estimation using ultra-wide band (UWB) signals between more than three reference terminals and the target node is widely used. In particular, a time of arrival (TOA)-based least square (LS) estimation is popular because the balanced performance in terms of calculation complexity and the accuracy is obtained. However, when the height of reference terminals and the target node is close, the three-dimensional LS-based estimation tends to fall into a local-minimum solution and it needs an accurate initial value of search to keep the estimation performance, resulting in the calculation complexity increase. Therefore, in this paper, we adopt a particle swarm optimization (PSO) method which effectively searches in wide-area space and propose an LS-based localization scheme using the combination of PSO and Newton-Raphson method achieving lower calculation complexity. The improved performances are shown by computer simulations.
机译:为了提供基于位置的服务(LBS),需要获得传感器网络中通信终端的精确位置。由于全球定位系统(GPS)的信号无法在室内接收,因此广泛使用在三个以上参考终端和目标节点之间使用超宽带(UWB)信号的基于三角测量的位置估计。特别地,基于到达时间(TOA)的最小二乘(LS)估计是流行的,因为获得了在计算复杂度和准确性方面的平衡性能。但是,当参考终端和目标节点的高度接近时,基于LS的三维估算往往会陷入局部最小解,并且需要精确的搜索初始值来保持估算性能,从而导致计算复杂度增加。因此,在本文中,我们采用了一种在广域空间中有效搜索的粒子群优化(PSO)方法,并结合PSO和Newton-Raphson方法提出了一种基于LS的定位方案,从而实现了较低的计算复杂度。计算机仿真显示了改进的性能。

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