The study used three filtering algorithms—complementary filtering, an improved two-layer Kalman filtering, and a fusion of both—to process and analyze the data.

If you’re looking to improve the stability of your self balancing robot you might use a simple horrifying equation like this one. It’s part of the journey [Lauszus] took when developing… ...

The Stanford-developed ReFIT (or Recalibrated Feedback Intention-Trained Kalman filter) algorithm harnesses this with a silicon chip which is implanted into the brain of the subject and records ...

We compare the Kalman filter algorithm to LSQR, an iterative algorithm proposed by Paige and Saunders (1982) for the solution of large-scale least-squares problems. LSQR explicitly exploits matrix ...

We consider the on-line Bayesian analysis of data by using a hidden Markov model, where inference is tractable conditional on the history of the state of the hidden component. A new particle filter ...