基于二次加权的LANDMARC景区改进定位算法研究
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摘要
针对景区地形复杂导致定位精度低的问题,在传统的LANDMARC室内定位算法基础上,提出了一种基于二次加权定位的改进算法,以解决景区中部分参考标签不能均布地复杂定位问题。首先,该算法通过一次加权定位求出待定位标签坐标;然后,将第一次求得的待定位坐标分别与参考区域的顶点连接,将参考区域划分为k个三角形区域,再分别求出这k个三角形内切圆的圆心;最后,以k个圆心作为最邻近参考坐标,通过设定二次加权系数,从而计算出更精确的待定位标签坐标。本文以某典型景区进行定位实验,实验仿真结果表明,改进后的定位算法在复杂的景区环境中,相比传统的一次加权定位算法,定位精度提高了10.6%,这说明其在复杂景区中具有更好的适用性。
Aiming at the problem of low positioning accuracy caused by complex terrains in scenic areas, we propose an improved two times weighted positioning algorithm based on the traditional indoor positioning algorithm LANDMARC to solve the complex positioning problem that some reference labels in scenic areas are not uniformly distributed. Firstly, the algorithm can obtain the coordinates of the labels to be located by one weighted positioning. Then, the obtained coordinates are connected with the vertexes of reference areas respectively. The reference area is divided into K triangular regions, and the centers of the inscribed circle of K triangular regions are obtained respectively. The K centers are taken as the reference coordinate of the nearest neighbor. By setting the coefficients of the second weighting, a more accurate positioning label coordinate is calculated. Experiments on a typical scenic area show that the improved positioning algorithm has a 10.6% improvement in positioning accuracy in the complex scenic environment compared with the traditional one-time weighted positioning algorithm, which proves its better applicability in complex scenic areas.
Aiming at the problem of low positioning accuracy caused by complex terrains in scenic areas, we propose an improved two times weighted positioning algorithm based on the traditional indoor positioning algorithm LANDMARC to solve the complex positioning problem that some reference labels in scenic areas are not uniformly distributed. Firstly, the algorithm can obtain the coordinates of the labels to be located by one weighted positioning. Then, the obtained coordinates are connected with the vertexes of reference areas respectively. The reference area is divided into K triangular regions, and the centers of the inscribed circle of K triangular regions are obtained respectively. The K centers are taken as the reference coordinate of the nearest neighbor. By setting the coefficients of the second weighting, a more accurate positioning label coordinate is calculated. Experiments on a typical scenic area show that the improved positioning algorithm has a 10.6% improvement in positioning accuracy in the complex scenic environment compared with the traditional one-time weighted positioning algorithm, which proves its better applicability in complex scenic areas.
引文
[1] Ju Ying. Study of indoor location algorithm based on RFID technology [D]. Tianjin: Tianjin University, 2010.(in Chinese)
[2] Yang Jun-hua, Li Yong, Cheng Wei. A novel algorithm for fingerprinting indoor localization based on K-correlation coefficient [J].Journal of Northwestern Polytechnical University, 2017,35(4):676-682.(in Chinese)
[3] Li Bao-shan, Yue Kang. Amendment and optimisation of LANDMARC localisation algorithm[J]. Computer Applications and Software, 2016,33(4):99-102.(in Chinese)
[4] Cao Jie, Niu Li-bo, Wang Jin-hua. An improved LANDMARC RFID indoor location algorithm[J]. Computer Engineering & Science, 2015,37(9):1671-1675.(in Chinese)
[5] Gu Li-jing, Gu Xiao-jie. Improved LANDMARC localization algorithm in inventory location management [J]. Computer Applications, 2014,34 (S1):315-317.(in Chinese)
[6] Zou Xue-yu, Han Fu-wei. An improved nearest neighbors algorithm based on LANDMARC[J]. Journal of Wuhan University (Science Edition), 2013,59(3):255-259.(in Chinese)
[7] Xu H, Ding Y, Li P, et al. An RFID Indoor positioning algorithm based on bayesian probability and k-nearest neighbor[J]. Sensors 2017, 17(8):1806.
[8] Kong Fan-zeng, Guo Min, Ren Xiu-kun, et al. GDOP study of the ray tracing based AOA positioning algorithm[J].Computer Engineering & Science, 2018,40(1):66-71.(in Chinese)
[9] Ding Tao, Yu Jie-yu, Liu Kai-hua, et al. A semi-definite programming approach to RSS-based localization in WSNs[J]. Computer Engineering & Science, 2017,39(12):2230-2235.(in Chinese)
[10] Huang Pei-chen, Luo Xi-wen, Zhang Zhi-gang, et al. Monocular vision agricultural machine localization based on pose threshold filte[J].Computer Engineering & Science,2018,40(1):93-100.(in Chinese)
[11] Yin Qiang, Zuo Lei, He Yi-gang, et al. RFID indoor localization based on support vector regression[J].Computer Engineering & Science, 2017,39(12):2340-2344.(in Chinese)
[12] Lei Kai. The research on the locating system of visitors in tourist scenic spot based on ZigBee[D]. Wuhan: Wuhan Textile University, 2013.
[13] Ma Ning. Research on RFID indoor wireless positioning algorithm based on RSSI [D]. Chengdu: University of Electronic Science and Technology, 2012.
[1] 俱莹.基于RFID的室内定位算法研究[D].天津:天津大学,2010.
[2] 杨军华,李勇,程伟.基于最邻近相关系数的指纹室内定位新算法[J].西北工业大学学报,2017,35(4):676-682.
[3] 李宝山,岳康.LANDMARC定位算法的修正与优化[J].计算机应用与软件,2016,33(4):99-102.
[4] 曹洁,牛丽波,王进花.一种改进LANDMARC射频识别室内定位算法[J].计算机工程与科学,2015,37(9):1671-1675.
[5] 顾李晶,顾小杰.改进的LANDMARC定位算法在库位管理中的应用[J].计算机应用,2014,34(S1):315-317.
[6] 邹学玉,韩付伟.基于LANDMARC的最近邻居改进算法[J].武汉大学学报(理学版),2013,59(3):255-259.
[8] 孔范增,郭敏,任修坤,等.基于射线跟踪的AOA定位算法的GDOP研究[J].计算机工程与科学,2018,40(1):66-71.
[9] 丁涛,于洁潇,刘开华,等.基于RSS的无线传感器网络半定规划定位算法研究[J].计算机工程与科学,2017,39(12):2230-2235.
[10] 黄沛琛,罗锡文,张智刚,等.基于姿态阈值滤波的单目视觉农业机械定位方法[J].计算机工程与科学,2018,40(1):93-100.
[11] 尹强,佐磊,何怡刚,等.基于支持向量回归机的RFID室内定位研究[J].计算机工程与科学,2017,39(12):2340-2344.
[12] 雷凯.基于ZigBee的景区游客定位系统的研究[D].武汉:武汉纺织大学,2013.
[13] 马宁.基于RSSI的RFID室内无线定位算法研究[D].成都:电子科技大学,2012.
[2] Yang Jun-hua, Li Yong, Cheng Wei. A novel algorithm for fingerprinting indoor localization based on K-correlation coefficient [J].Journal of Northwestern Polytechnical University, 2017,35(4):676-682.(in Chinese)
[3] Li Bao-shan, Yue Kang. Amendment and optimisation of LANDMARC localisation algorithm[J]. Computer Applications and Software, 2016,33(4):99-102.(in Chinese)
[4] Cao Jie, Niu Li-bo, Wang Jin-hua. An improved LANDMARC RFID indoor location algorithm[J]. Computer Engineering & Science, 2015,37(9):1671-1675.(in Chinese)
[5] Gu Li-jing, Gu Xiao-jie. Improved LANDMARC localization algorithm in inventory location management [J]. Computer Applications, 2014,34 (S1):315-317.(in Chinese)
[6] Zou Xue-yu, Han Fu-wei. An improved nearest neighbors algorithm based on LANDMARC[J]. Journal of Wuhan University (Science Edition), 2013,59(3):255-259.(in Chinese)
[7] Xu H, Ding Y, Li P, et al. An RFID Indoor positioning algorithm based on bayesian probability and k-nearest neighbor[J]. Sensors 2017, 17(8):1806.
[8] Kong Fan-zeng, Guo Min, Ren Xiu-kun, et al. GDOP study of the ray tracing based AOA positioning algorithm[J].Computer Engineering & Science, 2018,40(1):66-71.(in Chinese)
[9] Ding Tao, Yu Jie-yu, Liu Kai-hua, et al. A semi-definite programming approach to RSS-based localization in WSNs[J]. Computer Engineering & Science, 2017,39(12):2230-2235.(in Chinese)
[10] Huang Pei-chen, Luo Xi-wen, Zhang Zhi-gang, et al. Monocular vision agricultural machine localization based on pose threshold filte[J].Computer Engineering & Science,2018,40(1):93-100.(in Chinese)
[11] Yin Qiang, Zuo Lei, He Yi-gang, et al. RFID indoor localization based on support vector regression[J].Computer Engineering & Science, 2017,39(12):2340-2344.(in Chinese)
[12] Lei Kai. The research on the locating system of visitors in tourist scenic spot based on ZigBee[D]. Wuhan: Wuhan Textile University, 2013.
[13] Ma Ning. Research on RFID indoor wireless positioning algorithm based on RSSI [D]. Chengdu: University of Electronic Science and Technology, 2012.
[1] 俱莹.基于RFID的室内定位算法研究[D].天津:天津大学,2010.
[2] 杨军华,李勇,程伟.基于最邻近相关系数的指纹室内定位新算法[J].西北工业大学学报,2017,35(4):676-682.
[3] 李宝山,岳康.LANDMARC定位算法的修正与优化[J].计算机应用与软件,2016,33(4):99-102.
[4] 曹洁,牛丽波,王进花.一种改进LANDMARC射频识别室内定位算法[J].计算机工程与科学,2015,37(9):1671-1675.
[5] 顾李晶,顾小杰.改进的LANDMARC定位算法在库位管理中的应用[J].计算机应用,2014,34(S1):315-317.
[6] 邹学玉,韩付伟.基于LANDMARC的最近邻居改进算法[J].武汉大学学报(理学版),2013,59(3):255-259.
[8] 孔范增,郭敏,任修坤,等.基于射线跟踪的AOA定位算法的GDOP研究[J].计算机工程与科学,2018,40(1):66-71.
[9] 丁涛,于洁潇,刘开华,等.基于RSS的无线传感器网络半定规划定位算法研究[J].计算机工程与科学,2017,39(12):2230-2235.
[10] 黄沛琛,罗锡文,张智刚,等.基于姿态阈值滤波的单目视觉农业机械定位方法[J].计算机工程与科学,2018,40(1):93-100.
[11] 尹强,佐磊,何怡刚,等.基于支持向量回归机的RFID室内定位研究[J].计算机工程与科学,2017,39(12):2340-2344.
[12] 雷凯.基于ZigBee的景区游客定位系统的研究[D].武汉:武汉纺织大学,2013.
[13] 马宁.基于RSSI的RFID室内无线定位算法研究[D].成都:电子科技大学,2012.