摘要
针对蜂窝网络定位技术定位精度不高和在铁路实际中难以应用的缺点,提出一种新的列车定位方法。方法的核心是将速度传感器和蜂窝网的定位参数处理为若干列车可能的位置区域,并计算区域中列车位置出现的概率,以所有位置中出现概率最大为优化目标,建立列车定位的误差优化模型。在此基础上进一步引入遗传粒子群优化算法对模型进行求解,并用Matlab仿真。仿真结果表明:在复杂的铁路环境中,该定位方法对不同来源的误差有很好的吸收作用,是一种有效的定位方法。
Aiming at shortcomings of low positioning precision and difficulty of railway practical application, in railway scenario of existing cellular network localization technology, propose a new method of train positioning. The core of method is to process positioning parameters of speed sensor and cellular network, as possible position area of several trains and calculate probability of position of train in area, then use the maximum probability in all the positions as optimization objective, error optimization model is established for train positioning. The genetic particle swarm optimization （PSO） algorithm is introduced to solve the model and simulate by software Matlab. The simulation results show that in complex environment of railway, the localization method has good absorbing effect on errors of different sources and it is an effective positioning method.
出处
《传感器与微系统》
CSCD
2015年第5期157-160,共4页
Transducer and Microsystem Technologies
