基于CSI和FO-MKELM的室内定位方法  

Indoor Location Method Based on CSI and FO-MKELM

作　　者：邵小强[1,2] 杨永德 原泽文 李鑫 刘士博 马博 SHAO Xiaoqiang;YANG Yongde;YUAN Zewen;LI Xin;LIU Shibo;MA Bo(College of Electric and Control Engineering,Xi’an University of Science and Technology,Xi’an 710054,China;Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security,Xi’an 710054,China)

机构地区：[1]西安科技大学电气与控制工程学院,西安710054 [2]西安市电气设备状态监测与供电安全重点实验室,西安710054

出　　处：《电子科技大学学报》2024年第4期584-594,共11页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(52174198)。

摘　　要：针对Wi-Fi指纹定位精度低、维护繁琐、训练成本大的问题,提出一种基于信道状态信息(CSI)和改进多元核函数极限学习机(FO-MKELM)的室内定位方法。首先在预处理阶段对CSI幅值差和重构相位信息进行融合,以减少环境噪声的影响;其次,在离线训练阶段,采用分段式量子粒子群算法(QPSO)为模型寻找最优参数,以提高定位精度和泛化性能;然后,为抑制环境改变对定位性能的影响,引入在线增量学习和遗忘机制,添加部分新增数据进行增量学习持续更新定位模型,并设置数据有效期遗忘过旧数据减少不良影响;最终,在在线预测阶段,将模型输出与标签库进行匹配获得更为准确的坐标。在空旷楼道和复杂实验室两种不同的环境下进行实验验证,该算法相比其他定位方法在定位精度和长期稳定性上都有所提升。Aiming at the problems of low Wireless Fidelity(Wi-Fi)fingerprint location,cumbersome maintenance and high training cost,an indoor location method based on Channel State Information(CSI)and improved Multiple Kernel Extreme Learning Machine(FO-MKELM)was proposed.Firstly,the CSI amplitude difference and reconstructed phase information are fused in the preprocessing stage to reduce the influence of environmental noise.Secondly,in the off-line training stage,piecewise Quantum Particle Swarm Optimization(QPSO)is used to find the optimal parameters for the model to improve the positioning accuracy and generalization performance.Then,in order to suppress the impact of environment changes on positioning performance,the online incremental learning and forgetting mechanism is introduced,where some new data are added for incremental learning to continuously update the localization model,and the data validity period is set to forget old data to reduce adverse effects.Finally,in the online prediction stage,the model output is matched with the tag library data to obtain more accurate coordinates.The experimental results show that the static average errors of 0.39 m and 0.51 m are obtained in two different environments of open corridors and complex laboratories,respectively.Compared with other positioning methods,the proposed algorithm has improved positioning accuracy and long-term stability.

关 键 词：室内定位 信道状态信息 多元核极限学习机 在线增量学习 遗忘机制 量子粒子群 

分 类 号：TN92[电子电信—通信与信息系统]