Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond
Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …
devoted to time series state space models for a large variety of dynamic estimation …
Unbiased finite impluse response filtering: An iterative alternative to Kalman filtering ignoring noise and initial conditions
If a system and its observation are both represented in state space with linear equations, the
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations
State estimator design for a nonlinear discrete-time system is a challenging problem, further
complicated when additional physical insight is available in the form of inequality constraints …
complicated when additional physical insight is available in the form of inequality constraints …
Fault-tolerant sliding-mode-observer synthesis of Markovian jump systems using quantized measurements
This paper investigates the design problem of sliding mode observer (SMO) using quantized
measurements for a class of Markovian jump systems against actuator faults. Such a …
measurements for a class of Markovian jump systems against actuator faults. Such a …
[图书][B] Receding horizon control: model predictive control for state models
Receding Horizon Control introduces the essentials of a successful feedback strategy that
has emerged in many industrial fields: the process industries in particular. Receding horizon …
has emerged in many industrial fields: the process industries in particular. Receding horizon …
An iterative Kalman-like algorithm ignoring noise and initial conditions
YS Shmaliy - IEEE Transactions on Signal Processing, 2011 - ieeexplore.ieee.org
We address a p-shift finite impulse response (FIR) unbiased estimator (UE) for linear
discrete time-varying filtering (p= 0), p-step prediction (p>; 0), and p-lag smoothing (p<; 0) in …
discrete time-varying filtering (p= 0), p-step prediction (p>; 0), and p-lag smoothing (p<; 0) in …
Fast Kalman-like optimal unbiased FIR filtering with applications
S Zhao, YS Shmaliy, F Liu - IEEE Transactions on Signal …, 2016 - ieeexplore.ieee.org
In this paper, an optimal unbiased finite impulse response (OUFIR) filter is proposed as a
linking solution between the unbiased FIR (UFIR) filter and the Kalman filter (KF). We first …
linking solution between the unbiased FIR (UFIR) filter and the Kalman filter (KF). We first …
MEMS based pedestrian navigation system
In this paper we present a micro-electrical mechanical system (MEMS) based pedestrian
navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are …
navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are …
Receding-horizon estimation for discrete-time linear systems
A Alessandri, M Baglietto… - IEEE Transactions on …, 2003 - ieeexplore.ieee.org
The problem of estimating the state of a discrete-time linear system can be addressed by
minimizing an estimation cost function dependent on a batch of recent measure and input …
minimizing an estimation cost function dependent on a batch of recent measure and input …
Linear optimal FIR estimation of discrete time-invariant state-space models
YS Shmaliy - IEEE Transactions on Signal Processing, 2010 - ieeexplore.ieee.org
This paper addresses a general p-shift linear optimal finite impulse response (FIR) estimator
intended for solving universally the problems of filtering (p= 0), smoothing (p< 0), and …
intended for solving universally the problems of filtering (p= 0), smoothing (p< 0), and …