Kalman Filter with Augmented Measurement Model: An ECG Imaging Simulation Study

2013 ◽  
pp. 200-207 ◽  
Author(s):  
Walther H. W. Schulze ◽  
Francesc Elies Henar ◽  
Danila Potyagaylo ◽  
Axel Loewe ◽  
Matti Stenroos ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Simulation Study ◽  
Measurement Model ◽  
Imaging Simulation

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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.

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.

Multitarget geolocation via an agricultural octorotor based on orthographic projection and data association

2017 ◽  
Vol 232 (11) ◽  
pp. 2076-2090 ◽  
Author(s):  
Christian A Garcia ◽  
Yunjun Xu
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
Ad Hoc ◽  
Data Association ◽  
Measurement Model ◽  
Agricultural Fields ◽  
Orthographic Projection ◽  
Hover Flight ◽  
Computationally Intensive ◽  
First Time

In recent years, unmanned aerial vehicles with onboard spectral sensors have been used in detecting diseases in the agricultural fields. Geolocation, i.e. calculating the global coordinate of identified diseased regions based on images taken, is an important step in automating such a scouting operation. In this paper, the problem of geolocating multiple diseased regions in an image is studied. Based on the assumptions of stationary, two-dimensional shallow target plants and hover flight, an orthographic projection-based measurement model is developed. A probabilistic data association method is used to analyze the measurements from different target sources and a Kalman filter is designed to estimate the suspected diseased leaves’ position. To the best of the authors’ knowledge, it is the first time a data association technique is used in for locating multiple-diseased plants in agriculture applications. Additionally, the designed Kalman filter is based on conditions pertinent to small crops and is less computationally intensive than the typically used extended Kalman filter. Both simulation and ad hoc experiments are used to validate the proposed multitarget geolocation algorithm.

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Three Dimensional ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

An improved measurement variable estimation model for positioning mobile robot

2019 ◽  
Vol 20 (1) ◽  
pp. 78-101 ◽  
Author(s):  
Junsuo Qu ◽  
Leichao Hou ◽  
Ruijun Zhang ◽  
Zhiwei Zhang ◽  
Qipeng Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Manufacturing Industry ◽  
Unscented Kalman Filter ◽  
Measurement Model ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Estimation Model ◽  
Robot Positioning ◽  
Time Update

Abstract The localization and navigation technology are the key factors in the research of mobile robots. With the demand of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the accuracy of positioning results. This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved measurement model applied to UKF.

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