Kalman filtering has emerged as a pivotal tool in the field of multibody system dynamics, offering a robust framework for real-time state and parameter estimation in systems composed of interconnected ...

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 ...

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 ...

In configuring my Inertial Measurement Unit (IMU) for post-filtering of the data after the sensor, I see options for both a decimation FIR filter and also a Kalman filter. Which one is best for my ...

Numerical basics -- Method of least squares -- Recursive least-quares filtering -- Polynomial Kalman filters -- Kalman filters in a nonpolynomial world -- Continuous polynomial Kalman filter -- ...

[Jcparkyn] clearly had an interesting topic for their thesis project, and was conscientious enough to write up a chunk of it and release it to the wild. The project in question is a digital pen that ...

EnSilica, a leading independent provider of semiconductor solutions and IP, has launched a Kalman Filter acceleration IP core for use in situational awareness radar sensors for advanced driver ...

In practical situations, we observe the number of claims to an insurance portfolio but not the claim intensity. It is therefore of interest to try to solve the 'filtering problem'; that is, to obtain ...

This is an expository article that develops the Kalman filter from a Cholesky factorization perspective. In particular, the Kalman filter is shown to be a modification of the Cholesky factorization ...

If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...