一种减小TOA测量距离中NLOS影响的DMS模型和估计算法

有效的滤波方法和多尺度估计是无线定位技术的重要研究方向。为了减小在蜂窝网无线定位中非视距传 播(NLOS)对波达时间(TOA)测量距离的误差影响,先结合NLOS误差特性对标准Kalman滤波进行修正,然后提 出将不同采样率的修正Kalman滤波方程与多个尺度联系起来,建立了一种动态多尺度系统(DMS)模型,并给出基 于Haar小波的实现方法。仿真结果表明,基于上述方法优于直接进行Kalman滤波的效果,能较大幅度地提高 TOA测量距离的精度。

DMS model and estimation algorithm for mitigating effect of NLOS for TOA measurements

Effective filter and multiscale estimation are important research areas in wireless position location technology. In order to reduce the error effect of non-line of sight (NLOS) propagation for time of arrival (TOA) measurements, the Kalman filter was improved based on the analysis of the error speciality for NLOS. Then a dynamic multiscale system (DMS) was built by associating the different sampling based Kalman filter equation with multiscale, and the realization method using Haar wavelet was given. Simulation results show that the estimation effect of this method is better than that is gained by performing Kalman filter directly, and the method can markedly improve the range accuracy based on TOA measurements.