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 ...

This example estimates the normal SSM of the mink-muskrat data using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM ...

In this paper, the problem of channel estimation for Superposition Coded Modulation-Orthogonal Frequency Division Multiplexing (SCM-OFDM) systems over frequency selective channels is investigated.

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 ...

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 ...

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 ...

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 ...