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首页> 外文期刊>Analog Integrated Circuits and Signal Processing >ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation
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ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation

机译：基于ZigBee的室内定位系统，具有个人动态定位方法和改进的粒子滤波器估计

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摘要

We introduce a portable Wireless Sensor Network; which characterized by its great precision, fast detection, real time-monitoring and cheapness. The received signal strength indication (RSSI) is used for estimating the location of the target based on the trilateration algorithm. One of the biggest issues when acquiring a precise location is the numerous calculations that are required within particle filtering. Therefore, we have suggested a modified particle filtering (MPF) using a ZigBee model; in order to minimize both error and huge computations within the indoor environment based on the variance and gradient data-resampling. Increasing the particle weight near the estimated position using RSSI localization helps in avoiding undesired estimations. The MPF algorithm has been enhanced to predict a moving target within an indoor location with an average accuracy of approximately 1.5-2 m while consuming less power. The efficient number of particles has been improved, in addition to the estimated error; in comparison to the classical methods. The results prove that our algorithm can effectively meet the general indoor environmental demands with significant improvements over other algorithms and good position's evaluation.
机译：我们介绍了便携式无线传感器网络;其特点是其精度，快速检测，实时监测和廉价。接收的信号强度指示（RSSI）用于基于三边算法估计目标的位置。获取精确位置时最大的问题之一是粒子滤波中所需的许多计算。因此，我们建议使用ZigBee模型进行修改的粒子滤波（MPF）;为了基于方差和渐变数据重采样最小化室内环境中的误差和巨大计算。使用RSSI定位增加估计位置附近的粒度有助于避免不希望的估计。已经提高了MPF算法以预测室内位置内的移动目标，平均精度约为1.5-2μm，同时消耗更少的功率。除了估计的误差之外，还改善了有效数量的粒子;与古典方法相比。结果证明，我们的算法可以有效地满足普通的室内环境需求，在其他算法和良好的职位评价中具有显着改进。

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