Indoor Visible Light Localization Method Using TOA/RSS Hybrid Information
Objective Compared with wireless local area network(WLAN)/ultra wide band(UWB) and other electromagnetic wave wireless communication technologies, indoor visible light communication technology has the advantages of low cost and an advantageous edge. Research on the indoor localization method based on received signal strength(RSS), time of arrival(TOA), time difference of arrival(TDOA), angle of arrival(AOA), and other classical localization algorithms is the key to realizing localization technology. However, compared with traditional electromagnetic wave wireless communication technology, in the visible light communication environment, the localization methods based on most localization algorithms are not mature, and the improvement in the localization performance is often limited by the singular localization information. In this paper, TOA and RSS data are combined to reduce the effect of nonlinear errors on the localization accuracy in indoor visible light communication environments. Combined with the inertial sensing data at the receiving end, the robustness and low localization delay of the proposed localization method are guaranteed. Additionally, the localization accuracy of the system is further improved.
Methods Four sources are evenly distributed on the ceiling to simulate the indoor environment with a length and a width of 5m and a height of 3m. A channel model of indoor visible light communication is established, which has a direct line of the sight link and a multiple order reflection indirect line of the sight link. Then, the distribution of indoor received optical power is obtained, and the empirical formula of signal strength and distance is established using the mapping relationship between the received optical power and linear distance, between the source and receiver. Moreover, the time stamp record is used to measure the signal transmission time at the receiver. The particle filter based on an unscented Kalman filter is used to combine TOA and RSS data to improve the accuracy of distance estimation. Furthermore, the least-square method is used to estimate the localization coordinates. Finally, based on the inertial sensing data of the receiver, the movement trend is analyzed, and high-precision localization results are obtained.
Results and Discussions Based on the channel model, a localization simulation is conducted. A total of 625 test points are selected in the room, and the localization results are obtained by coordinate estimation. The indoor localization error fluctuates from 1.6 to 3.2cm, and the overall localization error is low lying with a small center and rising edge, exhibiting only a small fluctuation range. First, the simulation parameters are fixed, and the localization performance of the proposed localization method, RSS method based on trilateral localization, and traditional fingerprint localization method are compared. For the proposed localization method, the probability of the localization error at less than 3cm is 98.1% and the average and maximum localization errors are 2.02 and 3.39cm, respectively. For the traditional fingerprint localization method, the probability of the localization error at less than 3cm is 40.8% and the average and maximum localization errors are 3.11 and 6.12cm, respectively. For the RSS method based on trilateral localization, the probability of the localization error at less than 3cm is 1.6% and the average and maximum localization errors are 5.61 and 9.67cm, respectively. Second, the localization performance of 12-, 6-, and 3-W LEDs is compared. Under 12-W transmitting power, the probability of the localization error at less than 3cm is 98.1% and the average and maximum localization errors are 2.02 and 3.39cm, respectively. Under 6-W transmitting power, the probability of the localization error at less than 3cm is 91.2% and the average and maximum localization errors are 2.52 and 3.77cm, respectively. Under 3-W transmitting power, the probability of the localization error at less than 3cm is 42.4% and the average and maximum localization errors are 3.18 and 5.08cm, respectively. Finally, the localization time of the proposed localization method, RSS method based on trilateral localization, and traditional fingerprint localization method are compared. For this, 30 positioning processes of the three localization methods are selected. The RSS method based on trilateral localization exhibits the shortest localization time, while the localization time of the proposed localization method and fingerprint localization method fluctuates by approximately 1s.
Conclusions The simulation results are listed below:
1) Under fixed parameters, the maximum localization error of the proposed method is 44.61% less than that of the traditional fingerprint localization method, and the average localization error is reduced by 35.04%. Compared with the RSS method based on trilateral localization, the maximum and average localization errors of the proposed method are reduced by 64.94% and 63.99%, respectively. Therefore, the localization accuracy of the proposed localization algorithm is better than that of the other two localization methods.
2) The localization performance clearly decreases with a decrease in the LED transmitting power. However, no significant difference is observed between the performance of the proposed localization method at 3?W transmission power and that of fingerprint localization method at 12?W transmission power. Moreover, the proposed localization method shows better performance than the RSS method under 12?W transmission power, thus demonstrating the robustness of the proposed localization method.
3) The localization time of the proposed localization method is stable. Although it is nearly the same as that of the traditional fingerprint localization method, the overall trend is stable and it is only slightly longer than that of the RSS method based on trilateral localization. Thus, the proposed localization method can achieve an improved localization effect only by sacrificing a small amount of time resources.
In conclusion, the overall localization effect of the proposed method is good, and the localization error do not fluctuate significantly, thereby ensuring the robustness of the proposed localization method as well as low localization delay and good localization performance.
党宇超：重庆理工大学电气与电子工程学院, 重庆 400054
彭小峰：重庆理工大学电气与电子工程学院, 重庆 400054
李岳：重庆理工大学电气与电子工程学院, 重庆 400054
【1】Chen Q R, Zhang T. Light source layout optimization and performance analysis of indoor visible light communication system [J]. Acta Optica Sinica. 2019, 39(4): 0406003.
陈泉润, 张涛. 室内可见光通信系统的光源布局优化及性能分析 [J]. 光学学报. 2019, 39(4): 0406003.
【2】Jin Y C, Chen X B, Mao X R, et al. Influence of modulation degree on performances of visible light communication system [J]. Chinese Journal of Lasers. 2019, 46(5): 0506001.
靳永超, 陈雄斌, 毛旭瑞, 等. 调制度对可见光通信系统性能的影响 [J]. 中国激光. 2019, 46(5): 0506001.
【3】Panta K, Armstrong J. Indoor localisation using white LEDs [J]. Electronics Letters. 2012, 48(4): 228-230.
【4】Tsonev D, Chun H, Rajbhandari S, et al. A 3-Gb/s single-LED OFDM-based wireless VLC link using a gallium nitride μLED [J]. IEEE Photonics Technology Letters. 2014, 26(7): 637-640.
【5】Steendam H, Wang T Q, Armstrong J. Theoretical lower bound for indoor visible light positioning using received signal strength measurements and an aperture-based receiver [J]. Journal of Lightwave Technology. 2017, 35(2): 309-319.
【6】Yassin A, Nasser Y, Awad M, et al. Recent advances in indoor localization: a survey on theoretical approaches and applications [J]. IEEE Communications Surveys & Tutorials. 2017, 19(2): 1327-1346.
【7】Vatansever Z, Brandt-Pearce M. Visible light positioning with diffusing lamps using an extended Kalman filter[C]∥2017 IEEE Wireless Communications and Networking Conference (WCNC), March 19-22, 2017, San Francisco, CA, USA. New York: , 2017.
【8】Wang P F, Guan W P, Wen S S, et al. High precision indoor visiblethree-dimensional positioning system based on immune algorithm [J]. Acta Optica Sinica. 2018, 38(10): 1006007.
王鹏飞, 关伟鹏, 文尚胜, 等. 基于免疫算法的高精度室内可见光三维定位系统 [J]. 光学学报. 2018, 38(10): 1006007.
【9】Chen X C, Chu S, Li F, et al. Hybrid TOA and IMU indoor localization system by various algorithms [J]. Journal of Central South University. 2019, 26(8): 2281-2294.
【10】Li F M, Zhang T, Liu K, et al. An indoor positioning method based on range measuring and location fingerprinting [J]. Chinese Journal of Computers. 2019, 42(2): 339-350.
李方敏, 张韬, 刘凯, 等. 基于距离测量和位置指纹的室内定位方法研究 [J]. 计算机学报. 2019, 42(2): 339-350.
【11】Lain J K, Chen L C, Lin S C. Indoor localization using K-pairwise light emitting diode image-sensor-based visible light positioning [J]. IEEE Photonics Journal. 2018, 10(6): 7909009.
【12】Konings D, Faulkner N, Alam F, et al. FieldLight:device-free indoor human localization using passive visible light positioning and artificial potential fields [J]. IEEE Sensors Journal. 2020, 20(2): 1054-1066.
【13】Simon G, Zachar G, Vakulya G. Lookup: robust and accurate indoor localization using visible light communication [J]. IEEE Transactions on Instrumentation and Measurement. 2017, 66(9): 2337-2348.
【14】Alam F, Parr B, Mander S. Visiblelight positioning based on calibrated propagation model [J]. IEEE Sensors Letters. 2019, 3(2): 1-4.
【15】Keskin M F, Gezici S. Comparative theoretical analysis of distance estimation in visible light positioning systems [J]. Journal of Lightwave Technology. 2016, 34(3): 854-865.
【17】Wang T Q, Sekercioglu Y A, Neild A, et al. Position accuracy of time-of-arrival based ranging using visible light with application in indoor localization systems [J]. Journal of Lightwave Technology. 2013, 31(20): 3302-3308.
【18】Lei W Y, Chen B X, Yang M L, et al. Passive 3D target location method based on TOA and TDOA [J]. Systems Engineering and Electronics. 2014, 36(5): 816-823.
雷文英, 陈伯孝, 杨明磊, 等. 基于TOA和TDOA的三维无源目标定位方法 [J]. 系统工程与电子技术. 2014, 36(5): 816-823.
【19】Dong W J, Wang X D, Wu N. Ahybrid RSS/AOA algorithm for indoor visible light positioning [J]. Laser & Optoelectronics Progress. 2018, 55(5): 050603.
董文杰, 王旭东, 吴楠. 基于RSS/AOA混合的室内可见光定位算法 [J]. 激光与光电子学进展. 2018, 55(5): 050603.
【20】Wang X D, Wu N, Hu Q Q. Indoor visible light positioning based on multiple illuminated areas cooperation [J]. Journal of Optoelectronics·Laser. 2017, 28(4): 388-395.
王旭东, 吴楠, 胡晴晴. 多照明区域协作的室内可见光定位 [J]. 光电子·激光. 2017, 28(4): 388-395.
【21】Wang X D, Dong W J, Wu N. Hybrid TDOA/AOA algorithm based high accuracy indoor visible light positioning [J]. Systems Engineering and Electronics. 2019, 41(10): 2371-2377.
王旭东, 董文杰, 吴楠. 基于TDOA/AOA混合的高精度室内可见光定位算法 [J]. 系统工程与电子技术. 2019, 41(10): 2371-2377.
【22】Ghassemlooy Z, Popoola W, Rajbhandari S. Optical wireless communications: system and channel modelling with MATLAB[M]. Boca Raton: , 2019.
【23】Kahn J M, Barry J R. Wireless infrared communications [J]. Proceedings of the IEEE. 1997, 85(2): 265-298.
【24】Tian X H, Liao G S. An effective TOA-based location method for mitigating the influence of the NLOS propagation [J]. Acta Electronica Sinica. 2003, 31(9): 1429-1432.
田孝华, 廖桂生. 一种有效减小非视距传播影响的TOA定位方法 [J]. 电子学报. 2003, 31(9): 1429-1432.
【25】Xie H, Wei N. A simple NLOS error mitigation algorithm based on TDOA mobile location [J]. Journal of Harbin Engineering University. 2005, 26(1): 114-118.
谢红, 蔚娜. 基于TDOA的一种简化的非视距误差抑制算法 [J]. 哈尔滨工程大学学报. 2005, 26(1): 114-118.
【26】Xu T Y. TOA location algorithm in wireless sensor network under NLOS environment [J]. Computer Engineering. 2013, 39(12): 93-96.
徐彤阳. NLOS环境下无线传感器网络TOA定位算法 [J]. 计算机工程. 2013, 39(12): 93-96.
【27】Ye Z W, Ye H Y, Nie X Y, et al. High-accuracy visible light positioning method based on received signal strength indicator [J]. Chinese Journal of Lasers. 2018, 45(3): 0306002.
叶子蔚, 叶会英, 聂翔宇, 等. 基于接收信号强度检测的高精度可见光定位方法 [J]. 中国激光. 2018, 45(3): 0306002.
【28】Huang X P, Wang Y. Principle and application of Kalman filter: MATLAB simulation[M]. Beijing: Electronic Industry Press, 2015, 103-106.
黄小平, 王岩. 卡尔曼滤波原理及应用: MATLAB仿真[M]. 北京: 电子工业出版社, 2015, 103-106.
【29】Wu N, Wang X D, Hu Q Q, et al. Multiple LED based high accuracy indoor visible light positioning scheme [J]. Journal of Electronics & Information Technology. 2015, 37(3): 727-732.
吴楠, 王旭东, 胡晴晴, 等. 基于多LED的高精度室内可见光定位方法 [J]. 电子与信息学报. 2015, 37(3): 727-732.
【30】Cao Y P, Li X J, Hu Y Y. Visible light fingerprint-based high-accuracy indoor positioning method [J]. Laser & Optoelectronics Progress. 2019, 56(16): 160601.
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.
Cao Yang,Dang Yuchao,Peng Xiaofeng,Li Yue. Indoor Visible Light Localization Method Using TOA/RSS Hybrid Information[J]. Chinese Journal of Lasers, 2021, 48(1): 0106005
曹阳,党宇超,彭小峰,李岳. TOA/RSS混合信息室内可见光定位方法[J]. 中国激光, 2021, 48(1): 0106005