Filtering of Linear Systems With Unknown Inputs

2003 ◽  
Vol 125 (3) ◽  
pp. 482-485 ◽  
Author(s):  
Hosam E. Emara-Shabaik
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Linear Systems ◽  
Gaussian Noise ◽  
Unknown Inputs ◽  
Fast Tracking ◽  
Disturbance Estimation ◽  
Simulation Results ◽  
System States ◽  
Unknown Disturbance

State estimation of linear systems under the influence of both unknown deterministic inputs as well as Gaussian noise is considered. A Kalman like filter is developed which does not require the estimation of the unknown inputs as is customarily practiced. Therefore, the developed filter has reduced computational requirements. Comparative simulation results, under the influence of various types of unknown disturbance inputs, show the merits of the developed filter with respect to a conventional Kalman filter using disturbance estimation. It is found that the developed filter enjoys several practical advantages in terms of accuracy and fast tracking of the system states.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals A new approach to constrained state estimation for discrete-time linear systems with unknown inputs

2017 ◽  
Vol 28 (1) ◽  
pp. 326-341 ◽  
Author(s):  
Jose Fernando Garcia Tirado ◽  
Alejandro Marquez-Ruiz ◽  
Hector Botero Castro ◽  
Fabiola Angulo
Keyword(s):  
State Estimation ◽  
Linear Systems ◽  
Discrete Time ◽  
New Approach ◽  
Unknown Inputs ◽  
Time Linear

Self-tuning Kalman filter for compensation of transmission delay and loss in line-of-sight guidance

2018 ◽  
Vol 233 (11) ◽  
pp. 4191-4201
Author(s):  
Akram Nikfetrat ◽  
Reza Mahboobi Esfanjani
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Random Variables ◽  
Measurement Model ◽  
Line Of Sight ◽  
Uncertain Parameters ◽  
Suggested Approach ◽  
Self Tuning ◽  
Simulation Results ◽  
Sequence Simulation

A self-tuning Kalman filter is introduced to reduce the destructive effects of the delayed and lost measurements in the guidance systems employing command to line-of-sight strategy. A sequence of Bernoulli distributed random variables with uncertain probabilities are used to model the delayed and lost observations. Besides the state estimation, the uncertain parameters of the measurement model are identified online using the covariance of innovation sequence. Simulation results are given to demonstrate the merits of the suggested approach.

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

Iterated Cubature Kalman Filter for State Estimation of Maneuver Reentry Vehicle

2012 ◽  
Vol 466-467 ◽  
pp. 1329-1333
Author(s):  
Jing Mu ◽  
Chang Yuan Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Estimation Accuracy ◽  
Reentry Vehicle ◽  
Vehicle Simulation ◽  
Cubature Kalman Filter ◽  
Simulation Results ◽  
Iterate Process

We present the new filters named iterated cubature Kalman filter (ICKF). The ICKF is implemented easily and involves the iterate process for fully exploiting the latest measurement in the measurement update so as to achieve the high accuracy of state estimation We apply the ICKF to state estimation for maneuver reentry vehicle. Simulation results indicate ICKF outperforms over the unscented Kalman filter and square root cubature Kalman filter in state estimation accuracy.

scholarly journals Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization

2020 ◽  
Vol 10 (15) ◽  
pp. 5045 ◽  
Author(s):  
Ming Lin ◽  
Byeongwoo Kim
Keyword(s):  
Kalman Filter ◽  
Real World ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Localization Problem ◽  
Simulation Results ◽  
Non Gaussian ◽  
Self Driving Cars

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

scholarly journals Improvement of position and orientation of Unmanned Arial Vehicle (UAV) with INS/ GPS

2018 ◽  
Vol 7 (2.7) ◽  
pp. 642
Author(s):  
V Appala Raju ◽  
P Vasundhara ◽  
V ChandraKanth Reddy ◽  
A Sai Aiswarya
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
State Estimate ◽  
Simulation Results ◽  
Position And Orientation

This paper deals with the methods performing state estimation .that is position and orientation of Unmanned Arial Vehicle (UAV) using GPS, gyro, accelerometers and magnetometer sensors. Various methods are designed for position and orientation measurements of UAV. In this paper we proposed extended kalman filter based inertial navigation system using quaternions and 3D magnetometer. Initially we load UAV truth data from a file ,generate noisy UAV sensor measurements and perform UAV state estimation and display UAV state estimate results with proposed method compares with previously exited method extended  kalman filter based altitude and heading reference system using quaternion and 3D magnetometer simulation .Results shows that EKF-INS method gives better position and orientation of UAV.  

On the Application of Extended Kalman and Continuous Discrete Extended Kalman Filter for a HIL Magnetic Levitation Device

2007 ◽  
Author(s):  
John A. Henley ◽  
Panos S. Shiakolas ◽  
Kamesh Subbarao
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Magnetic Levitation ◽  
System Control ◽  
Simulated Environment ◽  
System States ◽  
Parameter Values ◽  
Reference Trajectories ◽  
Control Output

Magnetic levitation (maglev) devices have been extensively studied in the literature and find applications in many engineering fields. In this manuscript, we investigate and discuss the control and state estimation for a nonlinear maglev device based on Kalman estimation theory considering unmodeled process and measurement noise in the formulation. The Continuous Discrete Extended Kalman Filter (CDEKF) is utilized to estimate the system states and subsequently generate the control output based on the estimates. The approach is demonstrated in simulation using actual hardware (maglev and sensor) dynamics and parameter values. Currently, the proposed approach is being implemented and tuned on hardware in the loop (HIL) maglev device. The performance of the proposed approach in the simulated environment for state estimation and system control for step and sinusoidal reference trajectories and the HIL implementation procedure are also discussed.

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