Filtering for Linear Stochastic Systems With Small Measurement Noise

1995 ◽  
Vol 117 (3) ◽  
pp. 425-429 ◽  
Author(s):  
Z. Aganovic ◽  
Z. Gajic ◽  
X. Shen
Keyword(s):  
Kalman Filter ◽  
Riccati Equation ◽  
Stochastic Systems ◽  
Measurement Noise ◽  
Algebraic Riccati Equation ◽  
Reduced Order ◽  
Complete Decomposition ◽  
Algebraic Riccati Equations ◽  
Optimal Filters ◽  
Linear Stochastic Systems

In this paper we present a method which produces complete decomposition of the optimal global Kalman filter for linear stochastic systems with small measurement noise into exact pure-slow and pure-fast reduced-order optimal filters both driven by the system measurements. The method is based on the exact decomposition of the global small measurement noise algebraic Riccati equation into exact pure-slow and pure-fast algebraic Riccati equations. An example is included in order to demonstrate the proposed method.

Differential Games for Weakly Coupled Large-Scale Linear Stochastic Systems with an H∞-Constraint

2018 ◽  
Vol 20 (01) ◽  
pp. 1750025
Author(s):  
Hiroaki Mukaidani ◽  
Hua Xu
Keyword(s):  
Large Scale ◽  
Stochastic Systems ◽  
Necessary Conditions ◽  
Design Method ◽  
Infinite Horizon ◽  
Efficient Design ◽  
Reduced Order ◽  
Riccati Differential Equations ◽  
Weakly Coupled ◽  
Linear Stochastic Systems

A differential game approach for the finite-horizon stochastic control problem with an [Formula: see text]-constraint is considered for a class of large-scale linear systems. First, necessary conditions for the existence of a control strategy set are established by means of cross-coupled stochastic Riccati differential equations (CSRDEs). Second, an efficient design method to obtain a reduced-order parameter-independent approximate strategy set is proposed. Moreover, the performance degradation is estimated. Infinite-horizon case is also discussed. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed design scheme.

scholarly journals Indefinite LQ Control for Discrete-Time Stochastic Systems via Semidefinite Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Shaowei Zhou ◽  
Weihai Zhang
Keyword(s):  
Semidefinite Programming ◽  
Riccati Equation ◽  
Discrete Time ◽  
Time Horizon ◽  
Stochastic Systems ◽  
Positive Semidefinite ◽  
Algebraic Riccati Equation ◽  
Infinite Time ◽  
Lq Problem ◽  
Lq Control

This paper is concerned with a discrete-time indefinite stochastic LQ problem in an infinite-time horizon. A generalized stochastic algebraic Riccati equation (GSARE) that involves the Moore-Penrose inverse of a matrix and a positive semidefinite constraint is introduced. We mainly use a semidefinite-programming- (SDP-) based approach to study corresponding problems. Several relations among SDP complementary duality, the GSARE, and the optimality of LQ problem are established.

Parallel Optimal Kalman Filtering for Stochastic Systems in Multimodeling Form

1999 ◽  
Vol 122 (3) ◽  
pp. 542-550 ◽  
Author(s):  
Cyril Coumarbatch ◽  
Zoran Gajic
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Stochastic Systems ◽  
State Variables ◽  
State Transformation ◽  
Eighth Order ◽  
Reduced Order ◽  
Filtering Technique ◽  
Ill Conditioning

In this paper we show how to completely and exactly decompose the optimal Kalman filter of stochastic systems in multimodeling form in terms of one pure-slow and two pure-fast, reduced-order, independent, Kalman filters. The reduced-order Kalman filters are all driven by the system measurements. This leads to a parallel Kalman filtering scheme and removes ill-conditioning of the original full-order singularly perturbed Kalman filter. The results obtained are valid for steady state. In that direction, the corresponding algebraic filter Riccati equation is completely decoupled and solved in terms of one pure-slow and two pure fast, reduced-order, independent, algebraic Riccati equations. A nonsingular state transformation that exactly relates the state variables in the original and new coordinates (in which the required decomposition is achieved) is also established. The eighth order model of a passenger car under road disturbances is used to demonstrate efficiency of the proposed filtering technique. [S0022-0434(00)01703-2]

scholarly journals Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5808
Author(s):  
Dapeng Wang ◽  
Hai Zhang ◽  
Baoshuang Ge
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Stochastic Systems ◽  
Unscented Kalman Filter ◽  
Minimum Variance ◽  
Measurement Noise ◽  
Time Varying ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Sensor Information

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

Steady-State Marginalized Particle Filter for Attitude Estimation

2014 ◽  
Author(s):  
Yizhou Wang ◽  
Dennis Wai ◽  
Masayoshi Tomizuka
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Particle Filter ◽  
Stochastic Systems ◽  
Linear Part ◽  
Linear Structure ◽  
Attitude Estimation ◽  
Computational Time ◽  
Unit Quaternions ◽  
Linear Stochastic Systems

A marginalized particle filter (MPF) is designed for attitude estimation problem. Unit quaternions are used to parameterize rotations. The linear structure in the gyroscope bias dynamics enables us to completely decouple its evolution from quaternion particles. We further show that the linear part of the proposed MPF reaches a steady state, similar to what Kalman filter does for controllable and observable linear stochastic systems. Although the steady-state MPF is similar to the particle filter in structure, it has two advantages: (i) the theoretical superiority of marginalizing linear substructure, and (ii) the reduction in total computational time. Numerical simulations are performed to demonstrated the performance of the proposed filter.

scholarly journals The Extended Hamiltonian Algorithm for the Solution of the Algebraic Riccati Equation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhikun Luo ◽  
Huafei Sun ◽  
Xiaomin Duan
Keyword(s):  
Riccati Equation ◽  
Iteration Method ◽  
Learning Algorithm ◽  
Positive Definite ◽  
Gradient Algorithm ◽  
Algebraic Riccati Equation ◽  
Symmetric Matrices ◽  
Algebraic Riccati Equations ◽  
Subspace Iteration ◽  
Hamiltonian Algorithm

We use a second-order learning algorithm for numerically solving a class of the algebraic Riccati equations. Specifically, the extended Hamiltonian algorithm based on manifold of positive definite symmetric matrices is provided. Furthermore, this algorithm is compared with the Euclidean gradient algorithm, the Riemannian gradient algorithm, and the new subspace iteration method. Simulation examples show that the convergence speed of the extended Hamiltonian algorithm is the fastest one among these algorithms.

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