激光与光电子学进展, 2019, 56 (16): 160601, 网络出版: 2019-08-05   

基于可见光指纹的室内高精度定位方法 下载: 1243次

Visible Light Fingerprint-Based High-Accuracy Indoor Positioning Method
作者单位
1 桂林电子科技大学信息与通信学院认知无线电与信息处理教育部重点实验室,广西 桂林 541004
2 广西信息科学实验中心,广西 桂林 541004
摘要
考虑室内复杂环境对可见光定位精度的影响,提出了一种基于可见光指纹的室内定位方法。利用定位终端接收来自室内不同LED发出的信号强度信息,构建特征,将物理坐标作为标签,采用支持向量机回归(SVR)算法学习模型,确定移动目标粗略的位置范围。同时,为了进一步优化定位性能,以该位置范围作为限制条件,采用指纹定位算法实现更精确的定位。将所提定位方法在4 m×4 m×3 m的空间区域中进行了实验。结果表明,该方法定位误差小于1 cm的概率为67.5%,与SVR定位算法相比,平均定位精度提高了93.98%;与传统的基于指纹的定位方法相比,该方法可以在更低复杂度的情况下实现更精确的定位,有效提高了室内定位精度及其数据的利用率。
Abstract
Considering the influences of complex indoor environments on the accuracy of visible light positioning, an indoor positioning method based on the visible light fingerprint is proposed. This method uses a positioning terminal to receive the signal strength information generated by different LEDs in a room to construct features, and the physical coordinates are used as labels. Then, a support vector machine regression (SVR) algorithm learning model is adopted to determine the rough position range of a moving target. Simultaneously, to further optimize the positioning performance, a fingerprint positioning algorithm is used to achieve more accurate positioning with the location range as the limiting condition. The proposed positioning method is tested in a spatial region of 4 m×4 m×3 m. The results show that the probability of achieving a positioning error of less than 1 cm is 67.5%. Compared to the SVR positioning algorithm, the proposed method can improve the average positioning error by 93.98%. Compared to the traditional fingerprint-based localization method, the proposed method can achieve more accurate localization with lower complexity. Both the accuracy of the indoor positioning and the utilization rate of the data are effectively improved by the proposed method.

曹燕平, 李晓记, 胡云云. 基于可见光指纹的室内高精度定位方法[J]. 激光与光电子学进展, 2019, 56(16): 160601. Yanping Cao, Xiaoji Li, Yunyun Hu. Visible Light Fingerprint-Based High-Accuracy Indoor Positioning Method[J]. Laser & Optoelectronics Progress, 2019, 56(16): 160601.

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