paper-with-me

Papers

Relations Between Generalized JST Algorithm and Kalman Filtering Algorithm for Time Scale Generation

2023-08-24 · Yuyue Yan, Takahiro Kawaguchi, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki

In this paper, we present a generalized Japan Standard Time algorithm (JST-algo) for higher-order atomic clock ensembles and mathematically clarify the relations of the (generalized) JST-algo and the conventional Kalman filtering algorithm (CKF-algo) in the averaged atomic time and the clock residuals for time scale generation. In particular, we reveal the fact that the averaged atomic time of the generalized JST-algo does not depend on the observation noise even though the measurement signal is not filtered in the algorithm. Furthermore, the prediction error of CKF- algo is rigorously shown by using the prediction error regarding an observable state space. It is mathematically shown that when the covariance matrices of system noises are identical for all atomic clocks, considering equal averaging weights for the clocks is a necessary and sufficient condition to ensure equivalence between the generalized JST-algo and CKF-algo in averaged atomic time. In such homogeneous systems, a necessary and sufficient condition for observation noises is presented to determine which algorithm can generate the clock residuals with smaller variances. A couple of numerical examples comparing the generalized JST-algo and CKF-algo are provided to illustrate the efficacy of the results.

📄 PDF Abstract BibTeX arXiv:2308.12548

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

On the Relationship Between Iterated Statistical Linearization and Quasi-Newton Methods

2023-09-14 · Anton Kullberg, Martin A. Skoglund, Isaac Skog, Gustaf Hendeby

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi-Newton (QN) me…

Adaptive Kalman Filtering Developed from Recursive Least Squares Forgetting Algorithms

2024-04-16 · Brian Lai, Dennis S. Bernstein

Recursive least squares (RLS) is derived as the recursive minimizer of the least-squares cost function. Moreover, it is well known that RLS is a special case of the Kalman filter. This work presents the Kalman filter lea…

State Estimation

Smoothing Dynamic Systems with State-Dependent Covariance Matrices

2012-11-19 · Aleksandr Y. Aravkin, James V. Burke

Kalman filtering and smoothing algorithms are used in many areas, including tracking and navigation, medical applications, and financial trend filtering. One of the basic assumptions required to apply the Kalman smoothin…

Computational Efficiency

Remarks on stochastic cloning and delayed-state filtering

2025-08-28 · Tara Mina, Lindsey Marinello, John Christian arxiv

Many estimation problems in aerospace navigation and robotics involve measurements that depend on prior states. A prominent example is odometry, which measures the relative change between states over time. Accurately han…

Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks

2025-04-11 · Kundan Kumar, Muhammad Iqbal, Simo Särkkä

Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversaries compromise the system by injecting fa…

BlockingState EstimationState Space Models