Bootstrap filtering for the position location using wireless communication on highways

Sangwoo Cho, J. Chun
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Abstract

We propose a new position location algorithm based on the bootstrap filtering using the time difference of arrival measurements. The proposed algorithm imposes nonlinear kinematic constraints on the state estimates without destabilizing the algorithm. Such constraints can be most naturally incorporated in the Bayesian bootstrap filtering framework. The proposed algorithm is verified through simulation, and the result demonstrates that our algorithm is more robust than the extended Kalman filter and the bootstrap filter without constraints.
在高速公路上使用无线通信进行定位的自举滤波
提出了一种新的基于自举滤波的位置定位算法。该算法在不破坏算法稳定性的情况下对状态估计施加非线性运动学约束。这些约束可以最自然地纳入贝叶斯自举过滤框架。仿真结果表明,该算法比扩展卡尔曼滤波和无约束自举滤波具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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