Filtering Module on Satellite Tracking

[2304.04111] An Information-Based Micro-Kalman Filter for Satellite Tracking: A Comparative Study Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2304.04111 (eess) [Submitted on 8 Apr 2023 ( v1 ), last revised 19 Jun 2026 (this version, v3)] Title: An Information-Based Micro-Kalman Filter for Satellite Tracking: A Comparative Study Authors: Moh Kamalul Wafi View a PDF of the paper titled An Information-Based Micro-Kalman Filter for Satellite Tracking: A Comparative Study, by Moh Kamalul Wafi View PDF HTML (experimental) Abstract: Satellite dynamics and tracking remain important challenges in the context of space exploration and communication systems. Accurate state estimation is essential to maintain reliable orbital motion and system performance. This paper presents a mathematical framework for satellite state estimation based on a linearized model described by radial and angular states. The model incorporates two types of measurement noise corresponding to range and scaled angular deviations, which are assumed to be mutually independent with known covariance structures. The estimation problem is formulated using the Kalman filter, together with the associated Algebraic Riccati Equation (ARE), leading to both time-varying and steady-state solutions. In addition, a micro-Kalman filter ($\mu$KF) formulation is considered and compared with the classical Kalman filter, as well as with the extended Kalman filter (EKF), unscented Kalman filter (UKF), and an adaptive Kalman filter under a unified simulation setup. The results demonstrate that the proposed $\mu$KF achieves estimation performance nearly identical to that of the classical Kalman filter and its variants, with small and bounded estimation errors. The mean square estimation error (MSEE) remains low for all state variables under both noise configurations, confirming the effectiveness of the proposed approach for linear Gaussian systems. Comments: This version extends the previous version by including additional simulations, a comparative study with EKF, UKF, and adaptive Kalman filters, and enhanced trajectory visualization Subjects: Systems and Control (eess.SY) ; Dynamical Systems (math.DS) Cite as: arXiv:2304.04111 [eess.SY] (or arXiv:2304.04111v3 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2304.04111 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Moh Kamalul Wafi [ view email ] [v1] Sat, 8 Apr 2023 22:05:11 UTC (1,264 KB) [v2] Wed, 15 Apr 2026 01:38:51 UTC (753 KB) [v3] Fri, 19 Jun 2026 16:54:50 UTC (753 KB) Full-text links: Access Paper: View a PDF of the paper titled An Information-Based Micro-Kalman Filter for Satellite Tracking: A Comparative Study, by Moh Kamalul Wafi View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2023-04 Change to browse by: cs cs.SY eess math math.DS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Online Learning via Learned Latent Bayesian Tracking#1106258
• Long-term Online Tracking, T25 (GLES)#334472
• Long-term online tracking, cumulation 2009-2011 (GLES)#215532
• Perancangan dan pembuatan perangkat lunak yang menghasilkan adaptive background dengan metode gaussian mixture models untuk real-time tracking#1018560
• Simple online and realtime tracking#6591