This paper presents windowing and random weighting (WRW) based adaptive cubature Rauch Tung Striebel (CRTS) smoother (WRWACRTS). The Unscented KF (WRWUKF) has already existed as an alternative to nonlinear smoothing solutions. In the proposed method, both windowing and random weighted estimation methods are combined together and used to estimate the noise statistics. Subsequently, the weights of each window are adjusting randomly, and update the process and measurement noise covariances matrices at each epoch. The developed WRWACRTS algorithm overcomes the limitation of the conventional CKS. The Lyapunov function-based approach is used to investigate the convergence proof of the WRWACRTS algorithm. A numerical example is shown to demonstrate the performance of the proposed algorithm.

中文翻译：

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用于目标跟踪的基于窗口和随机加权的体积 RTS 平滑

本文介绍了基于加窗和随机加权 (WRW) 的自适应体积 Rauch Tung Striebel (CRTS) 平滑器 (WRWACRTS)。Unscented KF (WRWUKF) 作为非线性平滑解决方案的替代方案已经存在。在所提出的方法中，加窗和随机加权估计方法结合在一起并用于估计噪声统计。随后，每个窗口的权重随机调整，并在每个时期更新过程和测量噪声协方差矩阵。开发的 WRWACRTS 算法克服了传统 CKS 的局限性。基于李雅普诺夫函数的方法用于研究 WRWACRTS 算法的收敛性证明。给出了一个数值例子来证明所提出算法的性能。