...问题,在综合考虑传感器网络应用环境,节点的能量消耗,算法的计算量和通信量以及定位精度等因素的基础上,基于传统的残差加权(Rwgh)算法的思想,创新性地提出两种基于信号到达时间的残差加权定位算法:优选残差加权算法(ORwgh)和低计算量残差加权算法(LCC-Rwgh)。
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然后对位置估计结果使用残差加权算法进行NLOS误差抑制。
Then it USES Residual Weighting Algorithm to the result of estimation in order to restrain NLOS error.
提出了一种抑制多个NLOS误差的残差加权算法，通过仿真对比证明了该算法对多个NLOS误差具有更好的整体抑制效果。
Residual Weighting Algorithm to restrain much amount of NLOS error is proposed. Comparisions in the simulation show that this algorithm has better overall result to restrain much amount of NLOS error.
采用补充残差和加权平均的方法改进了传统的GM（1，1）模型，并将改进后的模型应用于产品故障数的预测。
The number of product trouble satisfies the properties like that. We improve traditional GM(1,1) model by supplying residual error and applying the improved model for forecasting of product trouble.
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