scholarly journals Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5864
Author(s):  
Qiupeng Wang ◽  
Xiaohui Sun ◽  
Chenglin Wen
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Design Method ◽  
Measurement Model ◽  
Higher Order ◽  
State Model ◽  
Network System ◽  
Maximum Correlation ◽  
Correlation Entropy

This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation.

Influence analysis of measurement errors in satellite attitude determination based on extended Kalman filter

2012 ◽  
Vol 116 (1178) ◽  
pp. 373-389
Author(s):  
Y. Jiao ◽  
J. Wang ◽  
X. Pan ◽  
H. Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Errors ◽  
Attitude Determination ◽  
Previous Analysis ◽  
Measurement Model ◽  
Experimental Results ◽  
Influence Analysis ◽  
Satellite Attitude ◽  
Structural Error

Abstract The satellite attitude determination approach based on the Extended Kalman Filter (EKF) has been widely used in many real applications. However, the accuracy of this method largely depends on the fitness of measurement model. We aim to analyse the influence of measurement errors to the accuracy of EKF based attitude determination approach in this paper. The measurement errors, which are divided into structural error and nonstructural error by their influences, are analysed in principle. In the setting of the combination of star sensors and gyros, according to the property of innovation, we employ the technique of correlation test to analyse the influences of different kinds of measurement errors. Experimental results demonstrate the effectiveness of our previous analysis.

scholarly journals Signal Processing for Tracking of Moving Object in Multi-Impulse Radar Network System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Beom-Hun Kim ◽  
Seung-Jo Han ◽  
Goo-Rak Kwon ◽  
Jae-Young Pyun
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Target Tracking ◽  
Extended Kalman Filter ◽  
Ultra Wideband ◽  
Network System ◽  
Real Time System ◽  
Positioning Systems ◽  
Tracking Method ◽  
Radar Network

Indoor positioning systems (IPSs) have been discussed for use in entertainment, home automation, rescue, surveillance, and healthcare applications. In this paper, we present an IPS that uses an impulse radio-ultra-wideband (IR-UWB) radar network. This radar network system requires at least two radar devices to determine the current coordinates of a moving person. However, one can enlarge the monitoring area by adding more radar sensors. To track moving targets in indoor environments, for example, patients in hospitals or intruders in a home, signal processing procedures for tracking should be applied to the raw data measured using IR-UWB radars. This paper presents the signal processing method required for robust target tracking in a radar network, that is, an iterative extended Kalman filter- (IEKF-) based object tracking method, which uses two IR-UWB radars to measure the coordinates of the targets. The proposed IEKF tracking method is compared to the conventional extended Kalman filter (EKF) method. The results verify that the IEKF method improves the performance of 2D target tracking in a real-time system.

scholarly journals Lightweight Extended Kalman Filter for MARG Sensors Attitude Estimation

2021 ◽  
Author(s):  
Lei Jing
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Process Model ◽  
Angular Rate ◽  
Measurement Model ◽  
Attitude Estimation ◽  
Human Motion ◽  
Computational Time ◽  
Orientation Estimation ◽  
Kinematic Equation

<div> <div> <div> <p>Low-power consumption of orientation estimation using low-cost inertial sensors are crucial for all the applications which are resource constrained critically. This paper presents a novel Lightweight quaternion-based Extended Kalman Filter (LEKF) for orientation estimation for magnetic, angular rate and gravity (MARG) sensors. In this filter, with employing the quaternion kinematic equation as the process model, we derived a simplified measurement model to create the lightweight system model for Kalman filtering, where the measurement model works efficiently and the involved computation of measurement model is reduced. It’s later proved that the proposed filter saves time consumption. Further, due to that no linearization is involved for the proposed measurement model, the good performance would be guaranteed in theory. For the experiments, a commercial sensor for data collection and an optical system to provide reference measurements of orientation, namely Vicon, are utilized to investigate the performance of the proposed filter. Evaluation for different application scenarios are considered, which primarily includes human motion capture and the drone application. Results indicate that the proposed filter provides reliable performance for both applications. What’s more, the comparison experiment shows that the proposed filter provides better performance in terms of either attitude estimation accu- racy or computational time. </p> </div> </div> </div>

scholarly journals A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Paula Cristiane Pinto Mesquita Pardal ◽  
Helio Koiti Kuga ◽  
Rodolpho Vilhena de Moraes
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Observation Model ◽  
Gps Measurements ◽  
Unscented Transformation ◽  
Sigma Point

Herein, the purpose is to present a Kalman filter based on the sigma point unscented transformation development, aiming at real-time satellite orbit determination using GPS measurements. First, a brief review of the extended Kalman filter will be done. After, the sigma point Kalman filter will be introduced as well as the basic idea of the unscented transformation, in which this filter is based. Following, the unscented Kalman filter applied to orbit determination will be explained. Such explanation encloses formulations about the orbit determination through GPS; the dynamic model; the observation model; the unmodeled acceleration estimation; also an application of this new filter approaches on orbit determination using GPS measurements discussion.

scholarly journals Load Parameter Identification for Parallel Robot Manipulator Based on Extended Kalman Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shijie Song ◽  
Xiaolin Dai ◽  
Zhangchao Huang ◽  
Dawei Gong
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Parallel Robot ◽  
Load Parameter ◽  
Identification Algorithm ◽  
Dynamic Parameters ◽  
Dynamic Coupling

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.

Sensor Noise Compensation for a Remote Controlled Sprayer Using EKF

2013 ◽  
Vol 694-697 ◽  
pp. 1025-1029
Author(s):  
Juh Yun An ◽  
In Nam Lee ◽  
Ki Ho Kim ◽  
Kwan Ho You
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
State Equation ◽  
Sensor Noise ◽  
Localization Accuracy ◽  
Noise Compensation ◽  
Precision Tracking ◽  
Skid Steering

The dynamic model of a remote controlled sprayer using skid-steering method is presented as a state equation. The precision tracking of the remote controlled sprayer is difficult to realize due to sensor noise. In this paper, we propose the extended Kalman filter (EKF) algorithm to compensate for the odometric sensor noise. To demonstrate the performance of the proposed algorithm, simulations which represent a real working sprayer in a greenhouse are performed. The results show the improved localization accuracy obtained by using the proposed algorithm.

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