基于正交多项式拟合的非视距定位优化算法

提出一种非视距环境中基于到达时间的移动定位优化算法。首先在基站端利用系统测量误差的先验知识判断到达时间测量值中是否存在非视距误差;然后通过加权正交多项式拟合对含有非视距误差的测量值进行修正,并利用有约束的加权优化算法对移动用户进行位置估计;最后对算法的定位误差性能进行仿真分析,并与视距环境中的最小二乘算法和有约束加权最小二乘算法的平均定位误差以及定位误差的克拉默·劳下界进行了比较。计算结果表明,提出的算法在非视距环境中能够得到较好的定位精度。

Optimization positioning algorithm in a non-line of sight environment based on orthogonal polynomial fitting

 A positioning algorithm based on time-of-arrival in a non-line of sight environment is presented. First, a priori knowledge of standard deviation of measurements noise was exploited to decide whether there were non line of sight errors in time-of-arrival measurements. Then a weighted orthogonal polynomial fitting technique was used to correct the measurements contaminated by non-line of sight errors and consequently give the estimation of location of  a mobile station. Finally, mean estimation errors by least squares algorithm and constrained weighted least squares algorithm in line-of-sight environment were calculated and the Cramér Rao lower bound was also given. Computation results show that the proposed algorithm can effectively reconstruct the range measurements and achieve better location accuracy in a non-line of  sight environment.