scholarly journals Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 337
Author(s):  
Máté Fazekas ◽  
Péter Gáspár ◽  
Balázs Németh
Keyword(s):  
Kalman Filter ◽  
Measurement Uncertainty ◽  
Nonlinear Dynamic ◽  
Autonomous System ◽  
Nonlinear Dynamic System ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Localization Accuracy ◽  
Lateral Dynamics ◽  
Effective System

Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

scholarly journals Application of Improved Simplex Quadrature Cubature Kalman Filter in Nonlinear Dynamic System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ting Cao ◽  
Huo-tao Gao ◽  
Chun-feng Sun ◽  
Yun Ling ◽  
Guo-bao Ru
Keyword(s):  
Kalman Filter ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Quadrature Rule ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Weighted Integral ◽  
Spherical Simplex

A novel spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is proposed to improve the estimation accuracy of nonlinear dynamic system. The nonlinear Gaussian weighted integral has been approximately evaluated using the spherical simplex rule and the arbitrary order Gauss–Laguerre quadrature rule. Thus, a spherical simplex Gauss–Laguerre cubature quadrature rule is developed, from which the general computing method of the simplex cubature quadrature points and the corresponding weights are obtained. Then, under the nonlinear Kalman filtering framework, the spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is derived. A high-dimensional nonlinear state estimation problem and a target tracking problem are utilized to demonstrate the effectiveness of the proposed spherical simplex Gauss–Laguerre cubature quadrature rule to improve the performance.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

Extended Routine Follow-up of Primary Uncomplicated Hip and Knee Arthroplasty is not Necessary

2019 ◽  
Vol 13 (1) ◽  
pp. 266-271
Author(s):  
Georgina Kakra Wartemberg ◽  
Thomas Goff ◽  
Simon Jones ◽  
James Newman
Keyword(s):  
Aseptic Loosening ◽  
Revision Surgery ◽  
Revision Arthroplasty ◽  
Cost Effective ◽  
Face To Face ◽  
Primary Surgery ◽  
Effective System ◽  
Knee Revision Surgery ◽  
Knee Revision

Aims: To create a more effective system to identify patients in need of revision surgery. Background: There are over 160,000 total hip and knee replacements performed per year in England and Wales. Currently, most trusts review patients for up to 10 years or more. When we consider the cost of prolonged reviews, we cannot justify the expenditure within a limited budget. Study Design & Methods: We reviewed all patients' notes that underwent primary hip and knee revision surgery at our institution, noting age, gender, symptoms at presentation, referral source, details of the surgery, reason for revision and follow up history from primary surgery. Results: There were 145 revision arthroplasties (60 THR and 85 TKR) that met our inclusion criteria. Within the hip arthroplasty group, indications for revision included aseptic loosening (37), dislocation (10), and infection (3), periprosthetic fracture, acetabular liner wear and implant failure. All thirty-seven patients with aseptic loosening presented with pain. Twenty-five were referred from general practice with new symptoms. The remaining were clinic follow-ups. The most common reason for knee revision was aseptic loosening (37), followed by infection (21) and then progressive osteoarthritis (8). Most were referred from GP as a new referral or were clinic follow-ups. All patients were symptomatic. Conclusion: All the patients that underwent revision arthroplasty were symptomatic. Rather than yearly follow up, we recommend a cost-effective system. We are implementing a 'non face-to-face' system. Patients would be directly sent a questionnaire and x-ray form. The radiographs and forms will be reviewed by an experienced arthroplasty surgeon. The concerning cases will be seen urgently in a face-to-face clinic.

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 Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

EEG Source Imaging (ESI) utility in clinical practice

2020 ◽  
Vol 65 (6) ◽  
pp. 673-682
Author(s):  
Pegah Khosropanah ◽  
Eric Tatt-Wei Ho ◽  
Kheng-Seang Lim ◽  
Si-Lei Fong ◽  
Minh-An Thuy Le ◽  
...  
Keyword(s):  
Focal Epilepsy ◽  
Cost Effective ◽  
Head Model ◽  
Computational Technique ◽  
Epileptogenic Zone ◽  
Clinical Tool ◽  
Source Imaging ◽  
Localization Accuracy ◽  
Eeg Source Imaging ◽  
Surgical Evaluation

AbstractEpilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73–91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.

scholarly journals Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiaqi Song ◽  
Haihong Tao
Keyword(s):  
Source Localization ◽  
Near Field ◽  
Nuisance Parameter ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Dual Polarization ◽  
Rank Reduction ◽  
Localization Accuracy ◽  
Noncircular Signals ◽  
Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

scholarly journals Compliance as a Cost-Effective System of Interaction Between Business and Government

2021 ◽  
Author(s):  
Nikolay I. Dorogov ◽  
Ivan A. Kapitonov ◽  
Nazygul T. Batyrova
Keyword(s):  
Cost Effective ◽  
Effective System
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