On the Statistical Errors of RADAR Location Sensor Networks with Built-In Wi-Fi Gaussian Linear Fingerprints
On the Statistical Errors of RADAR Location Sensor Networks with Built-In Wi-Fi Gaussian Linear Fingerprints
复制标题
内置Wi-Fi高斯线性指纹的雷达定位传感器网络统计误差研究
DOI：
10.3390/s120303605
复制
发表时间：
2012-03-01
期刊：
影响因子：
3.9
通讯作者：
Tian, Shuo
中科院分区：
文献类型：
--
作者：
Zhou, Mu;Xu, Yu Bin;Tian, Shuo
讨论了具有线性概率位置指纹的雷达传感器网络在不同Wi-Fi高斯强度下的期望误差。据我们所知，等和不等权雷达网络的统计误差已被建议作为一种更好的方式来评估不同的系统参数的行为和部署的参考点（RP）。然而，到目前为止，对统计误差、系统参数、RP个数和RP间隔之间的关系还没有足够的相关工作，更不用说计算相关的解析表达式了。因此，针对这一迫切问题，在一个简单的线性分布模型下，将更多的注意力放在线性期望误差，相邻数，RP数和区间的数学关系上，对数衰减模型中的参数和测试点（TP）处无线电信号强度（RSS）的变化，以构建更实用、更可靠的雷达定位传感器网络（RLSNs）并且还保证了在未来普遍存在的上下文感知环境中对基于位置的服务的准确性要求。此外，本文所讨论的误差理论的数值结果和一些真实的实验评估也将为我们未来的扩展分析。
The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis.