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针对室内接收信号强度定位具有较大误差的情况,提出一种高效的循环加权递推平均滤波算法对测量信号进行滤波.对已测量数据使用最小二乘法进行拟合得到多项式模型,并使用极大似然估计进行定位.实验结果表明,所提出的循环加权递推平均滤波算法在计算量较小的情况下,能够有效提高测距精度,多项式拟合比对数距离路径损耗模型拟合定位精度更高.在室内环境下,提出的算法定位精度达到0.6m左右,接近节点物理性能所允许的最佳定位精度.
Aiming at the situation that the indoor received signal intensity has a big error, a highly efficient recursive weighted average iterative filtering algorithm is proposed to filter the measured signal.Multiple polynomial models are fitted to the measured data using the least square method, Likelihood Estimation.Experimental results show that the proposed recursive weighted average iterative filtering algorithm can effectively improve the accuracy of ranging when the computational complexity is small and the polynomial fit logarithmic distance path loss model fits the positioning accuracy Higher.In the indoor environment, the proposed algorithm has a positioning accuracy of about 0.6m, which is close to the best positioning accuracy allowed by the physical performance of the node.