scholarly journals Analysis of Mobile 3-D Radar Error Registration when Radar Sways with Platform

2013 ◽  
Vol 67 (3) ◽  
pp. 451-472
Author(s):  
L. Chen ◽  
G.H. Wang ◽  
Y. He ◽  
I. Progri
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Lower Bound ◽  
State Vector ◽  
Time Varying ◽  
Mean Square ◽  
Dependent Variables ◽  
Simulation Results ◽  
New Variables ◽  
Mean Square Errors

For mobile radars installed on a gyro-stabilised platform (GSP) that can steadily follow an East-North-Up (ENU) frame, attitude biases (ABs) of the platform and offset biases (OBs) of the radar are linear dependent variables. Therefore ABs and OBs are unobservable in the linearized registration equations; however, when combining them as new variables, the system becomes observable, and this model has been called the unified registration model (URM). Unlike GSP mobile radars, un-stabilised GSP (or UGSP) mobile radars are installed on the platform directly and rotate with the platform simultaneously. For UGSP, it is testified that both types of biases are independent and observable because the time-varying attitude angles (AAs)1 of the platform are included in the registration equations, which destroy the dependencies of both kinds of biases and lead us to propose a completely different linearized registration model– the All Augmented Model (AAM). AAM employs all OBs and ABs in the state vector and a Kalman filter (KF) to produce their estimates. Numerical simulation results show that the estimated performance of AAM is close to the Cramér-Rao lower bound (CRLB) and that the Root Mean Square Errors (RMSEs) of the rectified measurements by using AAM are more than 500 m smaller than by URM in all directions.

scholarly journals State Vector Identification of Hybrid Model of a Gas Turbine by Real-Time Kalman Filter

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 659
Author(s):  
Gustavo Delgado-Reyes ◽  
Pedro Guevara-Lopez ◽  
Igor Loboda ◽  
Leobardo Hernandez-Gonzalez ◽  
Jazmin Ramirez-Hernandez ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Gas Turbine ◽  
State Vector ◽  
Convergence Time ◽  
Mean Square ◽  
Real Time Simulation ◽  
Time Simulation ◽  
The Mean ◽  
Mean Square Errors

A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

Numerical Simulation by CGM for Moving Force Identification

2012 ◽  
Vol 238 ◽  
pp. 826-829
Author(s):  
Zhen Chen ◽  
Jun Ling Han
Keyword(s):  
Numerical Simulation ◽  
Time Domain ◽  
Bending Moment ◽  
Identification Accuracy ◽  
Time Varying ◽  
Acceptable Solution ◽  
Moving Force ◽  
Ill Posed ◽  
Simulation Results ◽  
The Time Domain

The conjugate gradient method (CGM) is compared with the time domain method (TDM) in the paper. The numerical simulation results show that the CGM have higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-posed problems to some extent when they are used to identify the moving force. When the bending moment responses are used to identify the time-varying loads, the identification accuracy is more obviously improved than the TDM, which is more suitable for the time-varying loads identification.

Inference for Nonlinear Mapping with Sparse Fuzzy Rules Based on Multi-Level Interpolation

2011 ◽  
Vol 15 (3) ◽  
pp. 264-287 ◽  
Author(s):  
Kiyohiko Uehara ◽  
◽  
Shun Sato ◽  
Kaoru Hirota ◽  
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Rules ◽  
Nonlinear Mapping ◽  
Mean Square ◽  
Inference Method ◽  
Multi Level ◽  
Simulation Results ◽  
Conventional Methods ◽  
Rule Bases ◽  
Mean Square Errors

An inference method is proposed for sparse fuzzy rules on the basis of interpolations at a number of points determined by α-cuts of given facts. The proposed method can perform nonlinear mapping even with sparse rule bases when each given fact activates a number of fuzzy rules which represent nonlinear relations. The operations for the nonlinear mapping are exactly the same as for the case when given facts activate no fuzzy rules due to the sparseness of rule bases. Such nonlinear mapping cannot be provided by conventional methods for sparse fuzzy rules. In evaluating the proposed method, mean square errors are adopted to indicate difference between deduced consequences and fuzzy sets transformed by nonlinear fuzzy-valued functions to be represented with sparse fuzzy rules. Simulation results show that the proposed method can follow the nonlinear fuzzy-valued functions. The proposed method contributes to both reducing the number of fuzzy rules and providing nonlinear mapping with sparse rule bases.

scholarly journals Investigation of Chaotic and Strange Nonchaotic Phenomena in Nonautonomous Wien-Bridge Oscillator with Diode Nonlinearity

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
R. Rizwana ◽  
I. Raja Mohamed
Keyword(s):  
Numerical Simulation ◽  
Mean Square Displacement ◽  
Dynamical Variable ◽  
Mean Square ◽  
Maximal Lyapunov Exponent ◽  
Simulation Results ◽  
The One ◽  
Nonlinear Behaviors ◽  
Bifurcation Process ◽  
Electronic Oscillator

We have studied the chaotic and strange nonchaotic phenomena of a simple quasiperiodically forced Wien bridge oscillator circuit with diode as the only nonlinearity in this electronic oscillator system responsible for various nonlinear behaviors. Both the experimental results and the numerical simulation results for their confirmation are provided to show the bifurcation process. Various measures used for the numerical confirmation of SNA are power spectrum, maximal Lyapunov exponent, path of translational variables, mean square displacement, projection of poincaré section, log-log plot, and autocorrelation function. Based upon the numerical results, the birth of SNAs has been identified in the band merging route, intermittency route, and blowout bifurcation route. In addition, the birth of SNAs has been analyzed with peculiar mechanism, namely, “0-1 Test” employing the one state dynamical variable.

Dual Extended Kalman Filter Based Momentum Wheel Identification

2010 ◽  
Vol 29-32 ◽  
pp. 2278-2284
Author(s):  
Zhi Zhou Li ◽  
Rui Zhang ◽  
Zhen Cai Zhu ◽  
Xu Wen Liang
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Control ◽  
System Simulation ◽  
Control Subsystem ◽  
Simulation Results

As the most important actuator of the attitude control subsystem of the spacecraft, the momentum wheel should be precisely simulated. In this paper, Dual Extended Kalman filter is expressed and used to identify the momentum wheel system. In this way, the identification result can be used in the momentum wheel system simulation. This makes simulations run both fast and precisely. The numerical simulation results show that the algorithm can converge in given error.

Development of a Compact 1-D Micromanipulator with Flexure Manufactured Using Rapid Prototyping

2012 ◽  
Vol 2 (2) ◽  
pp. 47-57 ◽  
Author(s):  
Su Zhao ◽  
Yan Naing Aye ◽  
Cheng Yap Shee ◽  
Wei Tech Ang
Keyword(s):  
Finite Element ◽  
Rapid Prototyping ◽  
Root Mean Square ◽  
Piezoelectric Actuator ◽  
Trajectory Tracking ◽  
Nonlinear Stiffness ◽  
Mean Square ◽  
Manufacturing Costs ◽  
Simulation Results ◽  
Mean Square Errors

Presented is the design and initial experimental results of a compact 1-D micromanipulator. The presented manipulator is a piezoelectric actuator based complaint mechanism with a compact translational flexure manufactured using rapid prototyping. Rapid prototyping allows complicated designs with low manufacturing costs. Analytical and Finite Element (FE) models for designing the flexure are presented. The simulation results are compared with experiments conducted on a prototype. Nonlinear stiffness is measured and evaluated. The importance of pre-loading force is investigated. Trajectory tracking tests at different frequencies are performed on the manipulator. The maximum and root mean square errors are analyzed.

scholarly journals Time-Varying Noise Statistic Estimator Based Adaptive Simplex Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang ◽  
Dan Ding
Keyword(s):  
Kalman Filter ◽  
Maximum A Posteriori ◽  
Fading Memory ◽  
Time Varying ◽  
A Posteriori ◽  
Noise Statistic ◽  
Cubature Kalman Filter ◽  
Weighted Integral ◽  
Simulation Results ◽  
Posteriori Estimation

To address the problem that filtering accuracy is reduced with the inaccurate time-varying noise statistic in conventional cubature Kalman filter, a noise statistic estimator based adaptive simplex cubature Kalman filter is put forward in this paper. First, the simplex cubature rule is adopted to approximate the intractable nonlinear Gaussian weighted integral in the filter. Secondly, a suboptimal unbiased constant noise statistic estimator is derived based on the maximum a posteriori estimation criterion. For the time-varying noise, the above estimator is modified using an exponential weighted attenuation method to realize the oblivion of stale data which results in a fading memory estimator, which has the ability to estimate the time-varying noise statistic to revise the filter online. The simulation results indicate that the proposed filter can achieve higher accuracy than conventional filters with inaccurate noise statistic.

scholarly journals Extended Kalman Filter-Based Approach for Autonomous Synchronization and Ranging in GPS-Denied Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaobo Gu ◽  
Weiqiang Tan ◽  
Di Zhang ◽  
Yudong Lu ◽  
Ruidian Zhan
Keyword(s):  
Kalman Filter ◽  
Lower Bound ◽  
Extended Kalman Filter ◽  
Clock Synchronization ◽  
Exchange Mechanism ◽  
Clock Skew ◽  
Simulation Results ◽  
Cramer Rao Lower Bound ◽  
Clock Offset

Network ranging and clock synchronization based on two-way timing stamps exchange mechanism in complex GPS-denied environments is addressed in this paper. An estimator based on the Extended Kalman filter (EKF) is derived, according to which, the clock skew, clock offset, and ranging information can be jointly estimated. The proposed estimator provides off-line computation by storing the transmitting timing stamps in advance and could be implemented in asymmetrical and asynchronous scenarios. The simulation results show that the proposed estimator achieves a relative good performance than the existed estimators. In addition, a new Bayesian Cramér–Rao Lower Bound (B-CRLB) is derived. Numerous simulation results show that the proposed estimator meets the B-CRLB.

scholarly journals COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

2017 ◽  
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
State Vector ◽  
The State ◽  
Gps Data ◽  
Extended Kalman Filtering

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.

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