scholarly journals A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

2019 ◽  
Vol 8 (10) ◽  
pp. 3559-3563
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Continuous Time ◽  
Stochastic Dynamic ◽  
Time Dynamic ◽  
Time Dynamics ◽  
Hardware Realization ◽  
Aerial Vehicle ◽  
Non Linear System ◽  
Stochastic Dynamic Systems

The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

scholarly journals Study on the Initial Value Problem for Fractional-order Cubature Kalman Filters of Nonlinear Continuous-time Fractional-order Systems

2021 ◽  
Author(s):  
Chuang Yang ◽  
Zhe Gao ◽  
Yue Miao ◽  
Tao Kan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Fractional Order ◽  
Continuous Time ◽  
Kalman Filters ◽  
The State ◽  
Order System ◽  
Initial Value ◽  
Cubature Kalman Filter ◽  
Augmented State

Abstract To realize the state estimation of a nonlinear continuous-time fractional-order system, two types of fractional-order cubature Kalman filters (FOCKFs) designed to solve problem on the initial value influence. For the first type of cubature Kalman filter (CKF), the initial value of the estimated system are also regarded as the augmented state, the augmented state equation is constructed to obtain the CKF based on Grünwald-Letnikov difference. For the second type of CKF, the fractional-order hybrid extended-cubature Kalman filter (HECKF) is proposed to weaken the influence of initial value by the first-order Taylor expansion and the third-order spherical-radial rule. These two methods can effectively reduce the influence of initial value on the state estimation. Finally, the effectiveness of the proposed CKFs is verified by two simulation examples.

scholarly journals Identification and Stochastic Optimizing the UAV Motion Control in Turbulent Atmosphere

2021 ◽  
Vol vm02 (is02) ◽  
pp. 57-63
Author(s):  
Yevgeny Somov ◽  
Nikolay Rodnishchev ◽  
Tatyana Somova
Keyword(s):  
Stochastic Systems ◽  
Time Moment ◽  
Translational Motion ◽  
Stochastic Dynamic ◽  
Random Parameters ◽  
Nonlinear Stochastic Systems ◽  
Control Functions ◽  
Angular Velocities ◽  
Aerial Vehicle ◽  
Stochastic Dynamic Systems

In a class of diffusion Markov processes, we formulate a problem of identification of nonlinear stochastic dynamic systems with random parameters, multiplicative and additive noises, control functions, and the state vector at a final time moment. For such systems, the identifiability conditions are being studied, and necessary conditions are formulated in terms of the general theory of extreme problems. The developed engineering methods for identification and optimizing nonlinear stochastic systems are presented as well as their application for unmanned aerial vehicles under wind disturbances caused by atmospheric turbulence, namely, for optimizing the autopilot parameters during a rotary maneuver of an unmanned aerial vehicle in translational motion, taking into account the identification of its angular velocities.

The continuous–discrete extended Kalman filter revisited

2017 ◽  
Vol 32 (1) ◽  
Author(s):  
Gennady Yu. Kulikov ◽  
Maria V. Kulikova
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Nonlinear Filtering ◽  
Total Error ◽  
Waiting Times ◽  
Stochastic Dynamic ◽  
Extended Kalman Filtering ◽  
Gaussian Density ◽  
The Moment ◽  
Stochastic Dynamic Systems

AbstractThis paper elaborates a new approach to nonlinear filtering grounded in an accurate implementation of the continuous–discrete extended Kalman filter for estimating stochastic dynamic systems. It implies that the moment differential equations for calculation of the predicted state mean and error covariance of propagated Gaussian density are solved accurately, i.e., with negligible errors. The latter allows the total error of the extended Kalman filter to be reduced significantly and results in a new accurate continuous–discrete extended Kalman filtering method. In addition, this filter exploits the scaled local and global error controls to avoid any comparison of different physical units. The designed state estimator is compared numerically with continuous–discrete unscented and cubature Kalman filters to expose its practical efficiency. The problem of long waiting times (i.e., infrequent measurements) arisen in chemical and other engineering is also addressed.

scholarly journals Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 653
Author(s):  
Xiaohan Liu ◽  
Chenglin Wen ◽  
Xiaohui Sun
Keyword(s):  
Kalman Filter ◽  
Kronecker Product ◽  
Design Method ◽  
Digital Simulation ◽  
Gaussian White Noise ◽  
High Order ◽  
Stochastic Dynamic ◽  
Strongly Nonlinear ◽  
Intermediate Variables ◽  
Stochastic Dynamic Systems

In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic
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