用于无线定位的滤波算法研究

发布时间：2018-04-24 17:30

本文选题：无线定位 + 卡尔曼滤波　；参考：《中国计量学院》2015年硕士论文

【摘要】：无线定位技术是一种利用基站和移动台之间的信息,再使用定位算法对移动台进行位置估计的技术,其中定位算法是决定无线定位性能的关键因素。在无线定位算法中,非视距误差是导致各种定位算法性能不同的主要原因,因此这篇文章主要通过减弱非视距误差来提高定位算法的定位精度。卡尔曼滤波器是一种滤波性能很好的滤波器,它可以通过收集系统的观测信息和给出的预测信息,然后采用递归算法过滤掉其中包含的噪声,最终得到最优信息。基于卡尔曼滤波器的滤波功能,本文提出使用卡尔曼滤波器抑制无线定位中的非视距误差进而提高无线定位算法的定位性能。为了实现这一想法,本文首先在视距传播环境中使用卡尔曼滤波器滤除其中的测量噪声,然后把滤波后的数据应用到无线定位算法中得到初步定位结果。为了进一步提高定位性能,最后再次使用卡尔曼滤波器对初步定位结果进行优化处理,从而得到最优定位结果。由于非视距传播环境中的噪声比视距传播环境中的噪声复杂,为了抑制非视距误差,本文提出使用基于卡尔曼滤波器的联合卡尔曼滤波器进行处理以达到最优效果。同时,在使用联合卡尔曼滤波器基础上使用平滑技术对联合卡尔曼滤波器进行改进,以提高联合卡尔曼滤波器的滤波性能。最后仿真证明本文提出的改进方法在非视距误差分别服从高斯分布、均匀分布和指数分布的情况下定位精度提高了20.5米、25.2米和28.1米。
[Abstract]:The wireless location technology is a kind of technology which uses the information between the base station and the mobile station, and then uses the location algorithm to estimate the position of the mobile station, in which the location algorithm is the key factor to determine the wireless location performance. In the wireless location algorithm, the non-line-of-sight error is the main reason for the different performance of the various localization algorithms, so this paper mainly improves the location accuracy by reducing the non-line-of-sight error. Kalman filter is a kind of filter with good filtering performance. It can collect system observation information and give prediction information, then filter the noise contained therein by recursive algorithm, and finally get the optimal information. Based on the filtering function of Kalman filter, this paper proposes to use Kalman filter to suppress the non-line-of-sight error in wireless location and improve the localization performance of wireless location algorithm. In order to realize this idea, the Kalman filter is used to filter the measurement noise in the line-of-sight propagation environment, and then the filtered data is applied to the wireless location algorithm to obtain the initial location results. In order to further improve the localization performance, the Kalman filter is used to optimize the initial location results again, and the optimal location results are obtained. Because the noise in the non-line-of-sight propagation environment is more complex than that in the line-of-sight propagation environment, in order to suppress the non-line-of-sight error, a joint Kalman filter based on Kalman filter is proposed to achieve the optimal effect. At the same time, the United Kalman filter is improved by using smoothing technique to improve the filtering performance of the joint Kalman filter. Finally, the simulation results show that the proposed method can improve the positioning accuracy of 20.5 m 25.2 m and 28.1 m respectively from Gao Si distribution, uniform distribution and exponential distribution.
【学位授予单位】：中国计量学院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN713

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