Vehicle Localization utilizing a Novel Hybrid TDOA-Based Estimation

Oscar L. Owen, Zhenni Pan, S. Shimamoto
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Abstract

This research investigates the use of a hybrid technique to locate vehicle positions on a 2D plane solely via other vehicles to further the future realization of Vehicle-to-Vehicle (V2V) communication. An approach in which trilateration and Time Difference Of Arrival (TDOA) are combined to estimate the Direction Of Arrival (DOA) of an incoming signal is considered. By using TDOA measurements of receivers on the Receiver Vehicle (RV), estimation regions are constructed to robustly obtain the Transmitter Vehicle (TV) position. This proposal not only creates a method for TDOA to be directly used in V2V communication but compared to other localization methods such as TOA (Time Of Arrival), the proposed technique does not need to consider time synchronization between the TV and RV, allowing for usage in a larger variety of on-road scenarios. A regression model is also implemented to further improve the accuracy of the estimation. Evaluation of the proposal is conducted for same side DOA and opposing side DOA. The DOA estimation was compared with a theoretically ideal scenario incorporating TOA. For further clarification of the methods utility and to mimic the transmission signal in road environments, the proposal is also tested in a ray tracing propagation model. The simulations show that the proposed solution accompanied with the regression model estimated the DOA in a 1 nanosecond (ns) time step environment to 1.92° accuracy and 0.08°accuracy in a 0.1ns time step environment.
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基于新型混合tdoa估计的车辆定位
本研究探讨了一种混合技术的使用,仅通过其他车辆在二维平面上定位车辆位置,以进一步实现车对车(V2V)通信。提出了一种结合三边测量和到达时间差(TDOA)估计输入信号到达方向(DOA)的方法。利用接收车(RV)上接收机的TDOA测量值,构造估计区域,鲁棒地获得发射车(TV)的位置。该提议不仅创造了一种直接用于V2V通信的TDOA方法,而且与TOA(到达时间)等其他定位方法相比,所提出的技术不需要考虑电视和RV之间的时间同步,允许在更多种类的道路场景中使用。为了进一步提高估计的精度,还实现了回归模型。对提议的评估是针对同方方位和对方方位进行的。将DOA估计与包含TOA的理论理想场景进行了比较。为了进一步阐明该方法的实用性,并模拟道路环境中的传输信号,该建议还在光线追踪传播模型中进行了测试。仿真结果表明,该方法在1纳秒(ns)时间步长环境下的DOA精度可达1.92°,在0.1ns时间步长环境下的DOA精度可达0.08°。
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