摘要
通过统计学研究分析,传统的RSSI定位模型精度不高,对此提出了关于Walfisch-Ikegami模型对矿区节点定位的方法,即先对Walfisch-Ikegami模型公式进行简化,预测其路径损耗,并采用自适应遗传算法对其进行优化,求出基站和节点之间的距离。实验仿真分析表明,与传统的RSSI定位模型相比较,Walfisch-Ikegami模型确实提高了移动节点实时定位的精确性,该模型已应用到矿区,有很好的实用价值,取得了较好的效果。
According to the statistical analysis,the traditional RSSI positioning accuracy is not high. So proposes the method with Walfisch-Ikegami model to position mining nodes. First,the Walfisch-Ikegami model calculating formula is simplified,then using the simplified model formula to predict the path loss. And uses the adaptive genetic algorithm to optimize the model formula and gains the distances between base stations and nodes. Compares with the traditional RSSI positioning model,the experiment simulation analyzes show that the Walfisch-Ikegami model not only improves the accuracy of mobile node real-time positioning,but also has good practical value.
引文
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