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Improvement of bearings only target tracking using smoothing

机译:使用平滑功能改进仅轴承的目标跟踪

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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.
机译:通常使用线性化估计器(例如扩展卡尔曼滤波器(EKF))解决仅轴承目标跟踪问题。由于EKF算法在无源定位中性能不稳定,因此,基于修正增益EKF(MGEKF)和Rauch-Tung-Striebel(RTS),提出了一种新的滤波方法,称为平滑修正增益EKF(sMGEKF)。平滑。与被动定位中使用的传统方法(例如EKF和迭代EKF)相比,该方法在跟踪精度方面具有潜在优势。仿真研究证明了这种方法的优势。

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