Appendix 1: Relationships Between the Gaussian System (GS) and the System International (SI) for Electromagnetic Units

2013 ◽  
pp. 409-414
Keyword(s):  
Gaussian System

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

Identification of Non-Gaussian Stochastic System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Sung-man Park ◽  
O-shin Kwon ◽  
Jin-sung Kim ◽  
Jong-bok Lee ◽  
Hoon Heo
Keyword(s):  
Random Process ◽  
Random Noise ◽  
Stochastic Simulations ◽  
Kolmogorov Equation ◽  
Gaussian Random Process ◽  
System Parameters ◽  
Analysis Technique ◽  
Gaussian Analysis ◽  
Gaussian System ◽  
Non Gaussian

This paper proposes a method to identify non-Gaussian random noise in an unknown system through the use of a modified system identification (ID) technique in the stochastic domain, which is based on a recently developed Gaussian system ID. The non-Gaussian random process is approximated via an equivalent Gaussian approach. A modified Fokker–Planck–Kolmogorov equation based on a non-Gaussian analysis technique is adopted to utilize an effective Gaussian random process that represents an implied non-Gaussian random process. When a system under non-Gaussian random noise reveals stationary moment output, the system parameters can be extracted via symbolic computation. Monte Carlo stochastic simulations are conducted to reveal some approximate results, which are close to the actual values of the system parameters.

Review of Linear Stochastic Optimal Control Systems and Applications

1989 ◽  
Vol 111 (4) ◽  
pp. 399-403 ◽  
Author(s):  
H. V. Panossian
Keyword(s):  
Control Systems ◽  
Stochastic Optimal Control ◽  
Equivalence Principle ◽  
Additive Noise ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Basic Difference ◽  
Advantages And Disadvantages ◽  
Gaussian System ◽  
And Behavior

Many complex control systems can be modeled by linear ordinary differential/difference equations with multiplicative and additive noise. The characteristics and behavior of such systems are different from a regular linear quadratic Gaussian system, the basic difference being the inapplicability of the separation or the certainty equivalence principle. Control systems with multiplicative and additive noise are reviewed herein and the fundamental results, in continuous and discrete-time setting, are presented. Furthermore, the advantages and disadvantages are underlined and the need for further research is pointed out.

scholarly journals Pure Gaussian state generation via dissipation: a quantum stochastic differential equation approach

2012 ◽  
Vol 370 (1979) ◽  
pp. 5324-5337 ◽  
Author(s):  
Naoki Yamamoto
Keyword(s):  
Differential Equation ◽  
Steady State ◽  
Stochastic Differential Equation ◽  
Cluster State ◽  
Dissipative System ◽  
Complete Characterization ◽  
Gaussian State ◽  
Quantum State Transfer ◽  
Quantum Stochastic Differential Equation ◽  
Gaussian System

Recently, the complete characterization of a general Gaussian dissipative system having a unique pure steady state was obtained. This result provides a clear guideline for engineering an environment such that the dissipative system has a desired pure steady state such as a cluster state. In this paper, we describe the system in terms of a quantum stochastic differential equation (QSDE) so that the environment channels can be explicitly dealt with. Then, a physical meaning of that characterization, which cannot be seen without the QSDE representation, is clarified; more specifically, the nullifier dynamics of any Gaussian system generating a unique pure steady state is passive. In addition, again based on the QSDE framework, we provide a general and practical method to implement a desired dissipative Gaussian system, which has a structure of quantum state transfer.

Sign in / Sign up

Export Citation Format

Share Document