A comparison of extended Kalman filter, ultrasound time-of-flight measurement models for heating source localization

2012 ◽  
Vol 20 (7) ◽  
pp. 991-1016 ◽  
Author(s):  
M.R. Myers ◽  
A.B. Jorge ◽  
M.J. Mutton ◽  
D.G. Walker
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Source Localization ◽  
Time Of Flight ◽  
Heating Source ◽  
Measurement Models ◽  
Flight Measurement

scholarly journals Heat Source Localization Sensitivity Analyses for an Ultrasonic Sensor Array

2011 ◽  
Author(s):  
M. R. Myers ◽  
A. B. Jorge ◽  
D. G. Walker ◽  
M. J. Mutton
Keyword(s):  
Kalman Filter ◽  
Heat Source ◽  
Extended Kalman Filter ◽  
Source Localization ◽  
Time Of Flight ◽  
Ultrasonic Pulse ◽  
Sensitivity Analyses ◽  
Pulse Time ◽  
Heating Source ◽  
Measurement Models

State estimation procedures using the extended Kalman filter are investigated for a transient heat transfer problem in which a heat source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the other side of the plate. This work is an integral part of an effort to develop a system capable of locating the boundary layer transition region on a hypersonic vehicle aeroshell. Results from thermal conduction experiments involving one-way ultrasonic pulse time of flight measurements are presented. Uncertainties in the experiments and sensitivity to heating source location are discussed. Comparisons of heating source localization measurement models are conducted where ultrasonic pulse time of flight readings provide the measurement update to the extended Kalman filter. Two different measurement models are compared: 1) directly using the one-way ultrasonic pulse time of flight as the measurement vector and 2) indirectly obtaining distance from the one-way ultrasonic pulse time of flight and then using these obtained distances as the measurement vector in the extended Kalman filter. For the direct model, the Jacobian required by the extended Kalman filter is obtained numerically using finite differences from the finite element forward conduction solution. For the indirect model, the derivatives of the distances with respect to the state variables are obtained in closed form. Heating source localization results and convergence behavior are compared for the two measurement models. Two areas of sensitivity analyses are presented: 1) heat source location relative to sensor array position, and 2) sensor noise. The direct measurement model produced the best results when considering accuracy of converged solution, ability to converge to the correct solution given different initial guesses, and smoothness of convergence behavior.

Autonomous Aerial Refueling for UAVs Based on GPS/MV

2012 ◽  
Vol 433-440 ◽  
pp. 4087-4094 ◽  
Author(s):  
Long Wang ◽  
Xin Min Dong ◽  
Jun Guo ◽  
Hai Yan Jia
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Extended Kalman Filter ◽  
Reference Trajectory ◽  
Sensor Failure ◽  
Aerial Refueling ◽  
Control Laws ◽  
Measurement Models ◽  
Simulation Results ◽  
Docking Control

According to the UAV autonomous aerial refueling based on GPS/Machine Vision integration, the restrictions on the sensors during docking are analyzed. An adaptive Federal Kalman Filter (AFKF) is proposed, which is based on extended Kalman filter arithmetic, after modeling the sensors measurement models. Reference trajectory of docking is planed using cubic interpolators and docking control laws are designed with LQR. Simulation results show that the controller ensure the stabilized tracking and docking, and the AFKF outputs is continuous and stabilized during sensor failure comparing to centralize Kalman filter.

scholarly journals Non-Linear Filtering for Precise Point Positioning GPS/INS integration

2014 ◽  
Vol XL-2 ◽  
pp. 127-132 ◽  
Author(s):  
M. Abd Rabbou ◽  
A. El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Test Scenario ◽  
Linear Filters ◽  
Phase Measurements ◽  
Measurement Models ◽  
Nonlinear Motion ◽  
Non Linear ◽  
Tightly Coupled

This research investigates the performance of non-linear estimation filtering for GPS-PPP/MEMS-based inertial system. Although integrated GPS/INS system involves nonlinear motion state and measurement models, the most common estimation filter employed is extended Kalman filter. In this paper, both unscented Kalman filter and particle filter are developed and compared with extended Kalman filter. Tightly coupled mechanization is adopted, which is developed in the raw measurements domain. Un-differenced ionosphere-free linear combination of pseudorange and carrier-phase measurements is employed. The performance of the proposed non-linear filters is analyzed using real test scenario. The test results indicate that comparable accuracy-level are obtained from the proposed filters compared with extended Kalman filter in positioning, velocity and attitude when the measurement updates from GPS measurements are available.

A novel cubature statistically linearized Kalman filter for fractional-order nonlinear discrete-time stochastic systems

2017 ◽  
Vol 24 (24) ◽  
pp. 5880-5897 ◽  
Author(s):  
Hamed Torabi ◽  
Naser Pariz ◽  
Ali Karimpour
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Fractional Order ◽  
Discrete Time ◽  
Stochastic Systems ◽  
Taylor Series Expansion ◽  
The State ◽  
Nonlinear Functions ◽  
Measurement Models

In this paper, the state estimation problem for fractional-order nonlinear discrete-time stochastic systems is considered. A new method for the state estimation of fractional nonlinear systems using the statistically linearized method and cubature transform is presented. The fractional extended Kalman filter suffers from two problems. Firstly, the dynamic and measurement models must be differentiable and, secondly, nonlinearity is approximated by neglecting the higher order terms in the Taylor series expansion; by the proposed method in this paper, these problems can be solved using a statistically linearized algorithm for the linearization of fractional nonlinear dynamics and cubature transform for calculating the expected values of the nonlinear functions. The effectiveness of this proposed method is demonstrated through simulation results and its superiority is shown by comparing our method with some other present methods, such as the fractional extended Kalman filter.

Wind speed measurement based on time-of-flight estimation using Extended Kalman Filter

2013 ◽  
Author(s):  
Juan M. Mauricio Villanueva ◽  
Sebastian Y.C. Catunda ◽  
Raimundo Carlos S. Freire ◽  
Maxwell M. Costa ◽  
Nestor S. Castro Ingaroca
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
Extended Kalman Filter ◽  
Time Of Flight ◽  
Speed Measurement ◽  
Wind Speed Measurement

scholarly journals Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1584 ◽  
Author(s):  
Piotr Kaniewski
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kalman Filters ◽  
Linear Measurement ◽  
Linear Functions ◽  
Estimation Errors ◽  
Extended Kalman Filters ◽  
Measurement Models ◽  
Non Linear ◽  
Model Linearization

The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.

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