Multidimensional State Estimation With Multiple Composite Hypothesis Testing in the Presence of Interference

1988 ◽  
Vol 110 (3) ◽  
pp. 297-302
Author(s):  
K. Demirbas
Keyword(s):  
Hypothesis Testing ◽  
State Estimation ◽  
Dynamic Systems ◽  
The State ◽  
Composite Hypothesis ◽  
Time Simulation ◽  
Estimation Scheme ◽  
Component Estimation ◽  
Simulation Results ◽  
Memory Reduction

A new state estimation scheme is presented for multidimensional dynamic systems with arbitrary independent interference and noises, and is based upon quantization, multiple composite hypothesis testing, and a suboptimum decoding technique of Information Theory. The estimation of the state vector is sequentially done, component-by-component, in parallel and in blocks. “Component-by-component” estimation results in a considerable memory reduction for the implementation of the scheme, while estimation is blocks makes the implementation independent of time. Simulation results, some of which are presented, have shown that the new scheme performs well, whereas the classical estimation techniques are not, in general, applicable to the state estimation problem in the presence of arbitrary interference.

scholarly journals Trellis Representation and State Estimation for Dynamic Systems With a Kth Order Memory and Nonlinear Interference

1990 ◽  
Vol 112 (3) ◽  
pp. 517-519
Author(s):  
Kerim Demirbas¸
Keyword(s):  
Information Theory ◽  
State Estimation ◽  
Dynamic Systems ◽  
Dynamic Models ◽  
Sequential Algorithm ◽  
Fast State ◽  
Estimation Scheme ◽  
Trellis Diagram ◽  
Diagram Representation

A fast state estimation scheme is presented for dynamic systems with a Kth order memory and nonlinear interference. This new scheme is based upon a trellis diagram representation of dynamic models and stack sequential algorithm of Information Theory.

scholarly journals On the State Estimation of an Agonistic-Antagonistic Muscle System

2017 ◽  
Author(s):  
Thang Nguyen ◽  
Holly Warner ◽  
Hanieh Mohammadi ◽  
Dan Simon ◽  
Hanz Richter
Keyword(s):  
State Estimation ◽  
Control Method ◽  
The State ◽  
Reference Trajectory ◽  
State Variables ◽  
Muscle System ◽  
Spring Force ◽  
Antagonistic Muscle ◽  
Estimation Scheme ◽  
The Hill

In this paper, an agonistic-antagonistic muscle system is presented. This dual muscle system is based on the Hill muscle model. The problem of estimating the state variables and activation signals of the dual muscle system is addressed. A proposed estimation scheme which combines a super-twisting observer and an input estimator is given to provide a solution to the problem. A backstepping control method is used to track a reference trajectory. Numerical results are conducted to show that the relative error for state estimation is about 1% and that for the unknown inputs is about 3% when the measurements of the length of a muscle and its nonlinear spring force are affected by noise profiles whose normalized amplitude is 0.005.

Nonlinear estimator design based on extended Kalman filter approach for state estimation of articulated heavy vehicle

2018 ◽  
Vol 233 (2) ◽  
pp. 254-265 ◽  
Author(s):  
Hamze Ahmadi Jeyed ◽  
Ali Ghaffari
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
High Velocity ◽  
The State ◽  
Heavy Vehicle ◽  
Lane Change ◽  
Vehicle Model ◽  
Articulated Vehicle ◽  
Simulation Results

In this article, in order to measure the state variables directly in an articulated heavy vehicle, the extended Kalman filter approaches are proposed. For this purpose, using Kane’s method, a nonlinear model is developed for the articulated vehicle, including the motion equations of longitudinal, lateral, and yaw motion of the tractor, and the hitch articulation angle between the tractor and the semi-trailer. Using TruckSim software, the articulated vehicle model is verified through high-velocity lane change maneuver (a single sinusoidal wave with an amplitude of 5° and a frequency of 0.5 Hz) under the dry and slippery road condition. The simulation results showed that the proposed model is close to the real vehicle model and can be used in the estimator development. Then, the state estimation algorithm is designed and implemented using extended Kalman filter for real-time estimation of the states. To evaluate the performance of the extended Kalman filter, simulations with two maneuvers including high-velocity lane change maneuvers in the dry road and slippery road are carried out. The simulation results demonstrate the impressive performance of the extended Kalman filter for state estimation of the articulated vehicle in critical conditions such as the slippery road and the high velocity.

scholarly journals Constrained Dynamic Systems Estimation Based on Adaptive Particle Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Weili Xiong ◽  
Mingchen Xue ◽  
Baoguo Xu
Keyword(s):  
State Estimation ◽  
Nonlinear Model ◽  
Bayesian Approach ◽  
Dynamic Systems ◽  
Constrained Systems ◽  
Computational Time ◽  
Constrained Problems ◽  
Simulation Results ◽  
Non Gaussian ◽  
Bayesian Estimators

For the state estimation problem, Bayesian approach provides the most general formulation. However, most existing Bayesian estimators for dynamic systems do not take constraints into account, or rely on specific approximations. Such approximations and ignorance of constraints may reduce the accuracy of estimation. In this paper, a new methodology for the states estimation of constrained systems with nonlinear model and non-Gaussian uncertainty which are commonly encountered in practice is proposed in the framework of particles filter. The main feature of this method is that constrained problems are handled well by a sample size test and two particles handling strategies. Simulation results show that the proposed method can outperform particles filter and other two existing algorithms in terms of accuracy and computational time.

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