Indoor Positioning Algorithm of Subregional Visible Light Based on Multilayer ELM
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摘要:
在漫反射光信道中,可见光室内定位受一阶反射、噪声信号等的影响,边界区域的定位误差相比内部区域较大,针对此问题,提出一种基于多层极限学习机的分区域定位算法,并通过仿真实验验证了算法的有效性. 首先,对整体的实验区域建立第1层极限学习机神经网络,计算出整体的定位误差. 其次,根据定位误差的大小和分布特征建立第2层极限学习机神经网络,将整体实验区域划分为边界区域和内部区域. 对提取出的边界区域建立第3层极限学习机神经网络,计算出边界区域的定位误差. 最后将边界区域的定位误差更新到整体的定位误差中,以实现定位. 实验结果表明,该算法的整体平均定位误差为2.79 cm. 与接收信号强度算法和反向传播神经网络相比,该算法的平均定位误差分别降低了13倍和55.36%. 与单层极限学习机算法相比,边界区域的平均定位误差降低了65.66%,整体的平均定位误差降低了23.77%. 该算法边界区域的定位误差明显降低,具有更高的定位精度和鲁棒性能,可适用于不同的定位场景.
Abstract:
In a diffuse optical channel,the visible light indoor positioning is affected by first-order reflection and noise,and thus the positioning error in boundary region is relatively larger than that in interior region. To solve this problem,a positioning algorithm of subregional visible light indoor based on multilayer Extreme Learning Machine (ELM) was proposed in this paper,and the effectiveness of the proposed algorithm was verified by simulation experiments. Firstly,the first layer ELM based on the entire experimental region was established to calculate the entire positioning error. Secondly,the second layer ELM based on the magnitude and distribution characteristics of positioning error was established,and the entire experimental region was divided into boundary subregion and interior subregion. Thirdly,the third layer ELM based on the extracted boundary subregion was established to calculate the boundary positioning error. Lastly,the entire error with updated boundary error was used to realize the positioning. The experimental results show that the entire average positioning error of the proposed algorithm is 2.79 cm. Compared with the Received Signal Strength(RSS) and Back Propagation(BP) neural networks,the average positioning error is reduced by 13 times and 55.36%,respectively. Compared with the single-layer ELM,the boundary average positioning error is reduced by 65.66%,the entire average positioning error is reduced by 23.77%. Experimental results indicate that the boundary positioning error of the proposed algorithm is obviously decreased,which means the proposed algorithm has higher positioning accuracy and robustness,and is suitable for various positioning applications.