2015年8月1日 · A dual implementation of the Kalman filter is proposed for estimating the unknown input and states of a linear state-space model by using sparse noisy acceleration measurements. The successive structure of the suggested filter prevents numerical issues attributed to un-observability and rank deficiency of the augmented formulation of the problem.

双卡尔曼滤波算法的核心是将两种卡尔曼滤波算法结合起来。 其具体实现步骤： （1）系统初始化，赋予SOC和R0初值，得到状态变量X (k)和R0 (k)； （2）根据SOC初值，选定模型参数初值，即R1，R2和C1，C2； （3）确定两个卡尔曼滤波器中需要的矩阵，A (k)，C (k)等； （4）启动EKF算法，求得X’ (k)，并将实时数据传递给KF算法； （5）启动KF算法，利用求得X’ (k)计算出本时刻的R’ (k)； （6）将新求出的R’ (k)传递回给EKF算法，计算下一个时刻的X (k+1)； （7） …

We review several established meth ods in the linear case, and propose severa! extensions utilizing dual Kalman filters (DKF) and forward-backward (FB) filters that are applicable to neural networks. Methods are compared on several simulations of noisy time series. We also include an example of nonlinear noise reduction in speech.

We review several established meth ods in the linear case, and propose severa! extensions utilizing dual Kalman filters (DKF) and forward-backward (FB) filters that are applicable to neural networks. Methods are compared on several simulations of noisy time series. We also include an example of nonlinear noise reduction in speech.

Therefore, the Extended Kalman Filter or the Unscented Kalman Filter methods are typically used. In the dual estimation technique, which is suggested to have better convergence properties, two separate filters run concurrently: one that estimates the states of a model, and one that estimates the parameters.9 This allows the .

The model-based dual extended Kalman filter (DEKF) has been widely used for concurrent state of charge (SOC) and state of health (SOH) estimation. However, tuning the process and measurement covariance matrices of the DEKF is challenging and typically done through a trial and error process. In this work, a sleek version of

Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation.

2024年7月31日 · Therefore, one dual Kalman filter (KF) based on a single direction under a colored measurement noise (CMN) scheme is developed herein to improve the robustness and operational efficiency. The proposed method involves designing a data fusion model for the KF that integrates data from an inertial navigation system (INS) and ultrawideband (UWB).

2000年8月12日 · In the present article, we develop a novel dual Kalman-type filter, referred to as DEKF under minimum error entropy (MEE) with fiducial points (MEEFs-DEKF) to deal with the non-Gaussian...

In this work, we propose a state and parameter estimation approach based on the dual extended Kalman filter (DEKF) to obtain accurate states and time-varying parameters. The system is modeled as a linear parameter varying system.