IAGA模型支持下的灾区基站组网优化
发布时间：2019-04-23 20:06
【摘要】：根据矿井、失火办公大楼等灾区自然环境特征,结合基站信号多重覆盖的特点,提出了一种改进自适应遗传算法(IAGA),对基站组网部署进行优化。首先介绍灾害环境下的时间到达差算法(TDOA),用精度因子(DOP)刻画精度指标,提出了基站组网部署原则,以定位综合性能作为目标函数求解基站坐标。仿真试验表明,IAGA模型能够较好地应用于应急组网优化的部署,该算法基本能够达到最优解或次优解。
[Abstract]:According to the natural environment characteristics of disaster areas such as mine, fire office building and so on, an improved adaptive genetic algorithm (IAGA),) is proposed to optimize the network deployment of base stations according to the characteristics of multiple coverage of base station signals. First, the time-to-arrival difference algorithm (TDOA),) in disaster environment is introduced. The precision index is described by the precision factor (DOP), and the deployment principle of the base station network is put forward. The comprehensive positioning performance is used as the objective function to solve the base station coordinates. The simulation results show that the IAGA model can be applied to the deployment of emergency network optimization, and the algorithm can achieve the optimal or sub-optimal solution.
【作者单位】： 中国矿业大学国土环境与灾害监测国家测绘地理信息局重点实验室;中国矿业大学环境与测绘学院;华东交通大学理工学院经济管理分院;
【基金】：国家重点研发计划(2016YFC0803103)
【分类号】：TN929.5;TP18
，
本文编号：2463756
[Abstract]:According to the natural environment characteristics of disaster areas such as mine, fire office building and so on, an improved adaptive genetic algorithm (IAGA),) is proposed to optimize the network deployment of base stations according to the characteristics of multiple coverage of base station signals. First, the time-to-arrival difference algorithm (TDOA),) in disaster environment is introduced. The precision index is described by the precision factor (DOP), and the deployment principle of the base station network is put forward. The comprehensive positioning performance is used as the objective function to solve the base station coordinates. The simulation results show that the IAGA model can be applied to the deployment of emergency network optimization, and the algorithm can achieve the optimal or sub-optimal solution.
【作者单位】： 中国矿业大学国土环境与灾害监测国家测绘地理信息局重点实验室;中国矿业大学环境与测绘学院;华东交通大学理工学院经济管理分院;
【基金】：国家重点研发计划(2016YFC0803103)
【分类号】：TN929.5;TP18
，
本文编号：2463756
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