基于最大相关熵卡尔曼滤波的UWB室内定位算法  

Ultra wide band indoor location algorithm based on maximum correlation entropy Kalman filter

作　　者：张可鑫 席志红[1] ZHANG Kexin;XI Zhihong(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《应用科技》2024年第3期98-104,共7页Applied Science and Technology

摘　　要：随着无线通信技术的不断发展,室内定位技术逐渐成为关注的热点。然而,在复杂的室内环境中,如非高斯噪声等条件下,传统的超宽带(ultra wide band,UWB)定位算法往往存在定位精度低、鲁棒性差等问题。该算法将最大相关熵准则引入卡尔曼滤波(Kalman filter,KF)算法的代价函数中,并对测量噪声进行建模,因此能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,相对于传统的卡尔曼滤波算法具有更强的鲁棒性。在仿真实验中,采用了多个基站对运动目标进行定位,结果表明,非高斯环境下,相比于卡尔曼滤波和无迹卡尔曼滤波(unscented Kalman filter,UKF),新算法能够有效地提高室内定位的精度和鲁棒性。With the continuous development of wireless communication technology,indoor positioning technology has gradually become the focus of attention.However,in a complex indoor environment,such as non-Gaussian noise,traditional ultra wide band(UWB)localization algorithms often have problems such as low positioning accuracy and poor robustness.The proposed algorithm introduces the maximum correntropy criterion(MCC)into the cost function of the Kalman filter(KF)algorithm,and models the measurement noise,so it can assign a small weight to the anomaly measurement to reduce its influence on the state estimation,which is more robust than the traditional Kalman filter algorithm.In the simulation experiment,multiple base stations are used to locate the moving target.The results show that the new algorithm can effectively improve the accuracy and robustness of indoor positioning compared with Kalman filter and unscented Kalman filter(UKF)in a non-Gaussian environment.

关 键 词：室内定位 超宽带 卡尔曼滤波 最大熵准则 非高斯噪声 CHAN算法 Taylor算法 

分 类 号：TN98[电子电信—信息与通信工程]