Estimation theory
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An Analytical Study on Learning Difficulties in Estimation Theory Among Undergraduate Engineering Students
Abstract: Estimation Theory is a critical mathematical foundation for all engineering disciplines, enabling learners to model uncertainty, analyse signals, and derive optimal estimators. However, undergraduate students frequently struggle with its abstract properties, complex derivations, and prerequisite statistical concepts. This study investigates the key learning difficulties faced by students across multiple engineering branches using diagnostic tests, structured questionnaires, and interviews. The findings reveal that students commonly experience challenges related to weak probability …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article
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History and Applications of Kalman Filter: A Review
Abstract: The Kalman filter is a powerful algorithm that is used to estimate the dynamic system states with noisy measurements and uncertain behaviors. It is an optimal estimator that minimizes the average squared error between the estimated states and the true states, given the noisy data and a model of the system. The recursive algorithm is highly effective in tracking and predicting the state of complex systems over time. Kalman filters …
Published in International Journal of Electrical Power and Machine Systems · Vol. 2, Issue 1, 2024 · pp. 14–23 Read article