在一般对数衰减模型中衰减因子是一个常量，但在实际应用中会引起较大的测距定位误差. 为了减少定位估计误差，在对Zigbee 组网定位实验数据进行统计分析的基础上，提出用负指数函数来描述衰减因子与距离（目标节点与锚节点间距）之间的关系，进而建立一种改进对数衰减模型；给出一个基于改进对数衰减模型的ML 估计器，并推导了该估计器的Cramer-Rao下界(Cramer-Row lower bound, CRLB). 在实验室和车站站场的Zigbee 组网定位实验结果表明，使用改进对数衰减模型的ML 估计器能提供更准确的定位估计，对场景变化有较好的适应性.
To reduce estimation error caused by static path loss factor in a log path-loss model, a modified log path-loss model is proposed in this paper based on statistical analysis on the Zigbee localization experimental data. In this model, a negative exponent function is used to describe the distance relation of the path loss factor with target nodes and fixed nodes to improve performance of the traditional log path-loss model. A maximum likelihood (ML) estimator and the corresponding Cramer-Rao lower bounds is then proposed and derived. Results of Zigbee localization experiments in laboratory and bus station demonstrate good performance with accurate localization and flexibility for varying environments.
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