Zhang Haiqi, Cui Jiahe, Feng Lihui, Yang Aiying, Lv Huichao, Lin Bo, Huang Heqing
Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing 100081, China.
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2019 May 20;19(10):2324. doi: 10.3390/s19102324.
In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.
在这封信中，我们提出了一种基于接收信号强度（RSS）的室内可见光定位技术，该技术使用基于稀疏训练数据集的改进动量反向传播（MMBP）算法。与其他需要大量训练数据点才能准确定位的神经网络算法不同，我们在一个1.8米×1.8米×2.1米的定位区域内，仅用20个训练点就实现了对100个测试点的高精度定位。为了验证MMBP算法的适应性，我们通过实验展示了采用均匀或任意训练集的两种不同训练数据采集方法。此外，我们还展示了传统RSS算法的定位精度。实验结果表明，我们提出的算法针对任意集优化后的平均定位精度仅为1.88厘米，针对均匀集为1.99厘米，而传统RSS算法的平均定位误差达到14.34厘米。比较表明，我们提出的算法的定位精度高出7.6倍。结果还表明，我们系统的性能高于一些先前基于RSS和使用大量训练点训练的复杂机器学习算法的RSS指纹数据库的报告。
