An Improved Particle-Filter-Based Hybrid Optimization Algorithm for IoT Positioning in Uncertain WSNs
An Improved Particle-Filter-Based Hybrid Optimization Algorithm for IoT Positioning in Uncertain WSNs
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DOI：
10.1109/jiot.2023.3262753
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发表时间：
2023-08
影响因子：
10.6
通讯作者：
Chengming Luo;Xudong Yang;Xiyun Ge;Hao Liu;Cheng He;Gaifang Xin;Biao Wang
中科院分区：
文献类型：
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作者：
Chengming Luo;Xudong Yang;Xiyun Ge;Hao Liu;Cheng He;Gaifang Xin;Biao Wang
随着物联网技术在智能制造、安全生产、智慧城市等领域的广泛应用，目标位置感知已成为首要问题，可用于人员和物料定位、电子围栏设置、日常考勤统计、无线传感器网络（WSNs）的无处不在的感知和通信能力使得定位成为这些基于位置的物联网应用中不可或缺的一部分。针对无线传感器网络不确定性带来的较大定位误差问题，提出一种改进的基于粒子滤波的混合优化算法（IPFHO）。将含噪无线信号映射到不确定目标位置后，利用改进的粒子滤波实现移动的目标的初步定位，并采用迭代搜索方法进一步优化粒子滤波的粗定位精度。我们评估所提出的算法在不同的噪声水平，过程噪声方差和计算时间在广泛的模拟。结果表明，在存在无线测距误差和锚节点标定误差的情况下，该算法能够有效提高定位精度。与单纯滤波估计的定位误差0.27 m相比，滤波与搜索相结合的方法可以将定位误差减小到0.19 m。平台实验结果与仿真结果趋势一致，验证了该算法与相关定位算法相比具有上级精度。
With the wide applications of the Internet of Things (IoT) technologies in intelligent manufacturing, production safety, smart citie, and other fields, target location awareness has been the primary issue as it can be used to personnel and material positioning, electronic fence settings, daily attendance statistics, and so on. Wireless sensor networks (WSNs) positioning can become an indispensable part of these location-based IoT applications that benefit from the ubiquitous sensing and communication ability. For addressing the large positioning errors caused by uncertain WSNs, this article proposes an improved particle filter-based hybrid optimization (IPFHO) algorithm. After mapping the noisy wireless signal to uncertain target location, the preliminary positioning of mobile target is realized by improved particle filter, whose coarse accuracy over time can be further optimized with use of iterative search method. We evaluate the proposed algorithm under different noise levels, process noise variances and computation times in extensive simulations. The results indicate that the positioning accuracy of proposed algorithm can be effectively improved in the presence of wireless ranging errors and anchor node calibration errors. Compared with the positioning error 0.27 m estimated by pure filtering, the positioning error of proposed algorithm can be reduced to 0.19 m by combining the filtering and search methods. The platform experimental results, which are consistent with the trend of the simulation results, validate the superior accuracy of proposed algorithm compared with relevant positioning algorithms.