The Kalman Filter: Unveiling Precision in Dynamic System Tracking

Muhammad Salman

No p9es6, OSF Preprints from Center for Open Science

Abstract: The Kalman Filter, renowned for its versatility, assumes a pivotal role across an array of critical domains including control systems, signal processing, navigation, and robotics. Its adeptness at navigating complexity, uncertainty, and noise stems from the harmonious integration of a sophisticated mathematical model with real-world measurements, particularly excelling in contexts where ambiguity prevails and complete information remains elusive. Guided by an iterative framework, the filter continually refines its comprehension through data assimilation, displaying agility in adapting to evolving information and enhancing its portrayal of system dynamics. This dynamic process is encapsulated in its operational stages, which involve seamless fusion of historical data and mathematical modelling for state projection, coupled with the estimation of predictive uncertainties. Upon encountering new measurements, the Kalman Filter deftly computes the Kalman Gain, adeptly fusing measurements into state estimates. Notably, its influence extends beyond linear systems into nonlinear realms, leveraging innovations like the Extended Kalman Filter and Unscented Kalman Filter. The efficacy of the filter rests on rigorous adherence to assumptions and meticulous model construction, underscoring the importance of robust modelling and comprehensive evaluation. In the narrative of scientific and technological advancement, the Kalman Filter emerges as an exemplar of innovation, expertly navigating uncertainty, accommodating evolving data, and upholding enduring relevance across diverse spheres. Its harmonization of art and science in real-time state estimation solidifies its role as a steadfast guardian of precise understanding within a dynamic and ever-evolving world.

Date: 2023-08-08
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:p9es6

DOI: 10.31219/osf.io/p9es6

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