It appears that no particular approximate [nonlinear] filter is consistently better than any other, though . . . any nonlinear filter is better than a strictly linear one. 1 The Kalman filter is a ...

Kalman filtering has long served as a foundational tool for state estimation in dynamic systems, offering a robust and efficient means of filtering noise from measured signals. In the realm of ...

The problem of nonlinear filtering is studied asymptotically as the noise tends to zero. Detectability conditions ensuring that the filtering error tends to zero are ...

Fractional-order Kalman filtering extends traditional state estimation by incorporating fractional calculus, which enables the modelling of memory and hereditary properties in complex systems. This ...

As the final course in the Applied Kalman Filtering specialization, you will learn how to develop the particle filter for solving strongly nonlinear state-estimation problems. You will learn about the ...

In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. Prior knowledge needed: ECEA 5730, ECEA 5731, a Bachelor’s degree in ...

Distributed electric drive technology has become an important trend because of its ability to enhance the dynamic performance of multi-axle heavy vehicle. This article presents a joint estimation of ...