利用平滑改进轴承单目标跟踪

Zhang Qian, T. Song
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引用次数: 2

摘要

只有轴承的目标跟踪通常使用线性化估计器,如扩展卡尔曼滤波器(EKF)。针对EKF算法在被动定位中的不稳定性能,在修正增益EKF (MGEKF)和Rauch-Tung-Striebel (RTS)滤波的基础上,提出了一种新的滤波方法——平滑修正增益EKF (sMGEKF)。与传统的被动定位方法(如EKF和迭代EKF)相比,该方法在跟踪精度上具有潜在的优势。仿真研究表明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement of bearings only target tracking using smoothing
Bearings only target tracking is often addressed using linearized estimators such as the extended Kalman filter (EKF). Due to the erratic performance of the EKF algorithm in passive localization, a new filtering method, referred to as the smoothing modified gain EKF (sMGEKF), is proposed based on the modified gain EKF (MGEKF) and Rauch-Tung-Striebel (RTS) smoothing. Compared to the traditional approaches (e.g., the EKF and the iterated EKF) used in passive localization, the proposed method has potential advantages in tracking accuracy. A simulation study demonstrates the advantages of this approach.
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