论文标题
评估基于RS的RSSS基于RSS的目标定位，以进行紧急响应
Evaluation of RF Fingerprinting-Aided RSS-Based Target Localization for Emergency Response
论文作者
论文摘要
目标定位对于紧急派遣情况至关重要。最大似然估计（MLE）方法被广泛用于根据接收的信号强度测量值估算目标位置。但是，MLE求解器的性能受到初始化的显着影响（即解决方案或解决方案搜索空间的初始猜测）。为了解决这个问题，先前的一项研究提出了基于半决赛编程（SDP）的MLE初始化。但是，基于SDP的初始化技术的性能在很大程度上受阴影方差和目标和接收器之间的几何多样性的影响。在这项研究中，提出了基于指纹的MLE初始化的射频（RF）。此外，制定了组合RF指纹识别的目标定位的最大似然问题。在开放空间，城市和室内的三个测试环境中，与使用SDP初始初始初始的MLE算法相比，提议的RF指纹目标定位方法的性能提高了高达63.31％，平均为39.13％。此外，与SDP-MLE方法不同，在我们的实验中，该方法并未受到目标和接收器之间不良的几何形状的显着影响。
Target localization is essential for emergency dispatching situations. Maximum likelihood estimation (MLE) methods are widely used to estimate the target position based on the received signal strength measurements. However, the performance of MLE solvers is significantly affected by the initialization (i.e., initial guess of the solution or solution search space). To address this, a previous study proposed the semidefinite programming (SDP)-based MLE initialization. However, the performance of the SDP-based initialization technique is largely affected by the shadowing variance and geometric diversity between the target and receivers. In this study, a radio frequency (RF) fingerprinting-based MLE initialization is proposed. Further, a maximum likelihood problem for target localization combining RF fingerprinting is formulated. In the three test environments of open space, urban, and indoor, the proposed RF fingerprinting-aided target localization method showed a performance improvement of up to 63.31% and an average of 39.13%, compared to the MLE algorithm initialized with SDP. Furthermore, unlike the SDP-MLE method, the proposed method was not significantly affected by the poor geometry between the target and receivers in our experiments.
