Applying pattern recognition techniques based on hidden Markov models for vehicular position location in cellular networks
Applying pattern recognition techniques based on hidden Markov models for vehicular position location in cellular networks
复制标题
应用基于隐马尔可夫模型的模式识别技术进行蜂窝网络中的车辆位置定位
DOI：
10.1109/vetecf.1999.798435
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
S. Kyriazakos
中科院分区：
文献类型：
--
作者：
Stefan Mangold;S. Kyriazakos
讨论了蜂窝网络中用户位置的现场试验。所应用的车辆位置定位是基于模式识别和到达时间（TOA）测量的混合方法。模式识别由使用预测数据训练的隐马尔可夫模型 (HMM) 执行，以对特定区域的接收信号强度进行建模。 TOA 给出了活动移动设备所在位置的初步估计以及将使用哪组 HMM 进行位置估计。为了评估所提出的定位方法的准确性，我们使用汽车进行呼叫，行驶穿过单个 GSM 小区中的各个街道和定时提前 (TA) 区域。结果相当乐观；该解决方案可以满足许多用户定位应用的需求，而不需要对现有标准、基础设施或移动设备进行任何修改。
Field trials of subscriber locations in a cellular network are discussed. The vehicular position location applied is a hybrid method based on pattern recognition and time of arrival (TOA) measurements. The pattern recognition is performed by hidden Markov models (HMMs) trained with prediction data to model the strength of the received signals for particular areas. The TOA gives first estimations of where the active mobile is located and which set of HMMs is to be used for the position estimation. To assess the accuracy of the proposed location method, calls have been performed from a car, driving through various streets and timing advance (TA) zones in a single GSM cell. The results are quite optimistic; the solution may fulfil the demand of many subscriber location applications, without requiring any modifications of existing standards, infrastructure or the mobiles.