scholarly journals Passive Localization Using Time Difference of Arrival and Frequency Difference of ArrivalWC

2018 ◽  
Vol 06 (01) ◽  
pp. 65-73
Author(s):  
Xiansheng Guo ◽  
Yan Zhang ◽  
Botao Zeng
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Gang Li ◽  
Min Zhou ◽  
Hongwen Tang ◽  
Hongbin Chen

The low-orbit dual-satellite passive location system provides a cost-efficient and easy implementation platform, by which positions of unknown emitters on the Earth can be determined through measuring both the time and the frequency differences by two low-orbit satellites in space. However, in reality, this dual-satellite location system has low positioning accuracy because of the existence of systematic errors. In this paper, in order to address the problem of low positioning accuracy in low-orbit dual-satellite systems, a virtualization approach, consisting of the establishment of the virtual reference station and virtual frequency conversion, is proposed to correct systematic errors in the system. Specifically, we first analyze the coming source of systematic errors in the dual-satellite location system, and then, a virtual reference station and virtual frequency are constructed to correct errors in the measured time difference of arrival and the frequency difference of arrival, respectively. Simulation results show that systematic errors caused by the measured time difference of arrival can be significantly reduced, and the correction efficiency, defined as a ratio between remaining errors after implementing the proposed method over uncorrected ones, for the measured frequency difference of arrival, largely relies on both the virtual frequency and the transmission frequency of reference stations.


2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199177
Author(s):  
Ningning Qin ◽  
Chao Wang ◽  
Changxu Shan ◽  
Le Yang

In this study, an interval extension method of a bi-iterative is proposed to determine a moving source. This method is developed by utilising the time difference of arrival and frequency difference of arrival measurements of a signals received from several receivers. Unlike the standard Gaussian noise model, the time difference of arrival - frequency difference of arrival measurements are obtained by interval enclosing, which avoids convergence and initialisation problems in the conventional Taylor-series method. Using the bi-iterative strategy, the algorithm can alternately calculate the position and velocity of the moving source in interval vector form. Simulation results indicate that the proposed scheme significantly outperforms other methods, and approaches the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, the interval widths of the results provide the confidence degree of the estimate.


2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876737
Author(s):  
Pengwu Wan ◽  
Yongjing Ni ◽  
Benjian Hao ◽  
Zan Li ◽  
Yue Zhao

Passive localization of the wireless signal source attracts a considerable level of research interest for its wide applications in modern wireless communication systems. To accurately locate the signal source passively in the downtown area, sensors are carried on the unmanned aerial vehicles flying in the air, where the wireless sensor network can be established with an optimal geometry configuration conveniently. In this case, the influence of multipath fading can be avoided and the time difference of arrival measurement can be estimated precisely in Rician channel. By employing the operating center as a calibration source to refine the positions of the unmanned aerial vehicles, we present a simplified formulation of the time difference of arrival localization method according to the min-max criterion. To accurately estimate the position of the source, the nonlinear equations are relaxed using semidefinite programming to obtain an initial solution, which is utilized as the starting point of the iterative algorithm to refine the solution. In the simulation section, the validity and the robustness of the proposed methods are verified through the performance comparison with the Cramer–Rao lower bound.


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