3 citations · 3 across the 6 of their papers we have counts for
6 papers
An Adaptive Kalman Filter that Learns the Coloring Dynamics of the Process Noise
Mohammad Almuhaihi, Dennis Bernstein
In many applications of state estimation, the process noise is colored; this case is addressed by applying the standard Kalman filter (KF) to dynamics that are augmented with the c…
Recursive Least Squares with Fading Regularization for Finite-Time Convergence without Persistent Excitation
Brian Lai, Dimitra Panagou, Dennis S. Bernstein
This paper extends recursive least squares (RLS) to include time-varying regularization. This extension provides flexibility for updating the least squares regularization term in r…
Adaptive Kalman Filtering Developed from Recursive Least Squares Forgetting Algorithms
Brian Lai, Dennis S. Bernstein
Recursive least squares (RLS) is derived as the recursive minimizer of the least-squares cost function. Moreover, it is well known that RLS is a special case of the Kalman filter.…
Convergence of Recursive Least Squares Based Input/Output System Identification with Model Order Mismatch
Brian Lai, Dennis S. Bernstein
Discrete-time input/output models, also called infinite impulse response (IIR) models or autoregressive moving average (ARMA) models, are useful for online identification as they c…
SIFt-RLS: Subspace of Information Forgetting Recursive Least Squares
Brian Lai, Dennis S. Bernstein
This paper presents subspace of information forgetting recursive least squares (SIFt-RLS), a directional forgetting algorithm which, at each step, forgets only in row space of the…
Efficient Batch and Recursive Least Squares for Matrix Parameter Estimation
Brian Lai, Dennis S. Bernstein
Traditionally, batch least squares (BLS) and recursive least squares (RLS) are used for identification of a vector of parameters that form a linear model. In some situations, howev…