An NLOS Environment Location Algorithm Based on Geometric Constraint and Iteration
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摘要: 针对在非视距 （non-line-of-sight，NLOS）环境中传统最优化定位算法抗NLOS误差能力较弱、且需要一个较准确的初始估计位置以确保算法收敛这一问题，提出一种应用在双基站场景下的基于几何约束及迭代的定位算法. 通过引入最大散射半径作为几何约束条件，以线性迭代方式进行一维全局搜索，并采用最小二乘算法获得移动台（mobile station，MS）初始估计位置，然后利用设定的阈值门限对各初始位置点进行筛选，最后通过加权平均获得MS的最终估计位置. 仿真结果表明：当散射半径为200 m时，本文算法的定位误差在200 m以下的概率能达到100%；在相同环境下，本文算法计算时间开销仅是网格搜索法的0.4%.Abstract: In non-line-of-sight (NLOS) environments, the traditional optimal localization algorithm is weak against NLOS errors and needs an accurate initial position value to guarantee the algorithm converge. To deal with this, a positioning algorithm based on geometric constraint and iteration in a scenario of two base station (BS) is proposed. By introducing the maximum scattering radius as the geometric constraint condition, the linear iterative method is used to perform a one-dimensional global search, and the initial estimation positions of mobile station (MS) are obtained by the least squares (LS) algorithm. Then the initial MS estimation positions are filtered by a distance threshold value, and finally its final position is obtained by weighted average. Simulation results show that when the scattering radius is 200 m, the probability of location error under 200 m can reach 100%, and in the same environment, the calculation time of this algorithm is only 0.4% of the grid search algorithm.
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Key words:
- non-line-of-sight (NLOS) /
- geometric constraint /
- iteration /
- least squares /
- weighted average
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图 5 MLE与有无散射半径约束的变化曲线
Figure 5. MLE variation with or without scattering radius constraint
表 2 算法时间开销
Table 2. Algorithm time cost
s 算法 IPA GSA 本文迭代算法 约束条件 1 0.1786 0.1350 0.0059 约束条件 2 0.1644 0.1087 0.0050 
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