Positioning performance analysis of the time sum of arrival algorithm with error features

Positioning performance analysis of the time sum of arrival algorithm with error features
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具有误差特征的到达时间和算法的定位性能分析

DOI:
10.1007/s11801-018-7196-9
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发表时间:
2018-03
影响因子:
0.9
通讯作者:
马艳秋
马艳秋
中科院分区:
物理与天体物理4区
文献类型:
--
作者:
宫峰勋;马艳秋

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到达时间差(TDOA)算法的理论定位精度很高。但在实际应用中存在一些问题。本文从均方根误差(RMSE)和几何精度因子(GDOP)两个方面分析了加性白色高斯噪声(AWGN)环境下时间到达和(TSOA)算法的定位性能。构建了TSOA本地化模型。利用该方法,给出了4个基站的位置模糊域分布。然后,从4个基站出发,通过计算RMSE和GDOP的变化,分析了定位性能。随后,当基站数量、基站布局等定位参数发生变化时,给出了TSOA定位算法的性能变化规律。从而揭示了TSOA的定位特性和性能。从RMSE和GDOP状态变化趋势来看,TSOA定位算法具有较好的抗噪性能和鲁棒性。TSOA的抗噪声性能将用于减少MLAT系统的盲区和错误定位率。
The theoretical positioning accuracy of multilateration (MLAT) with the time difference of arrival (TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival (TSOA) algorithm from the root mean square error (RMSE) and geometric dilution of precision (GDOP) in additive white Gaussian noise (AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating theRMSEand GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From theRMSEand GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.
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