Planning of refracted starlight observation based on observability analysis

2021 ◽  
pp. 095441002110301
Author(s):  
Chengzhen Wu ◽  
Xueying An ◽  
Dingjie Wang ◽  
Hongbo Zhang
Keyword(s):  
Fisher Information ◽  
Information Matrix ◽  
Quantitative Relationship ◽  
Navigation Problem ◽  
Information Index ◽  
Observability Analysis ◽  
Observation Sequence ◽  
Fisher Information Index ◽  
Selection Of ◽  
Observation Scheme

In traditional observation schemes of stellar refraction navigation, the accuracy was limited due to unreasonable observation directions. In order to ameliorate this situation, a method of refracted starlight observation based on observability analysis is proposed. The function of this method is optimally generating an observation attitude sequence according to standard trajectories of spacecraft so that the selection of a refracted starlight observation sequence can be realized. Specifically, the improvement of Fisher information matrix calculation enables this method to be qualified for the navigation problem with unsteady measurement quantities as well as the non-fully observability which is defined as the capability of estimating the system state through measurements in finite time. Here, we construct a quantitative relationship between refracted starlight measurements and system observability by means of Fisher information index ( FII). Next, the observation scheme is retrieved by searching the maximum value of the optimized variable, which includes the ( FII). Finally, we resort to the extended Kalman filter to accomplish typical trajectory navigation simulations of the observation scheme. The results indicate that our method brings more accuracy than traditional ones in estimation of position and velocity of the optimal observation scheme.

scholarly journals The Maximal Strichartz Family of Gaussian Distributions: Fisher Information, Index of Dispersion, and Stochastic Ordering

2016 ◽  
Vol 2016 ◽  
pp. 1-17
Author(s):  
Alessandro Selvitella
Keyword(s):  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Stochastic Order ◽  
Stochastic Ordering ◽  
Gaussian Distributions ◽  
Information Index ◽  
The Family ◽  
Statistical Dispersion ◽  
Fisher Information Index

We define and study several properties of what we callMaximal Strichartz Family of Gaussian Distributions. This is a subfamily of the family of Gaussian Distributions that arises naturally in the context of theLinear Schrödinger Equationand Harmonic Analysis, as the set of maximizers of certain norms introduced by Strichartz. From a statistical perspective, this family carries with itself some extrastructure with respect to the general family of Gaussian Distributions. In this paper, we analyse this extrastructure in several ways. We first compute theFisher Information Matrixof the family, then introduce some measures ofstatistical dispersion, and, finally, introduce aPartial Stochastic Orderon the family. Moreover, we indicate how these tools can be used to distinguish between distributions which belong to the family and distributions which do not. We show also that all our results are in accordance with the dispersive PDE nature of the family.

scholarly journals The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
S. Borguet ◽  
O. Léonard
Keyword(s):  
Fisher Information ◽  
Health Monitoring ◽  
Performance Monitoring ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Engine Performance ◽  
Essential Elements ◽  
Sensor Selection ◽  
Present Contribution ◽  
Selection Of

Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other hand, the issue of sensor selection allowing an efficient diagnosis has received less attention from the community. The present contribution revisits the problem of sensor selection for engine performance monitoring within the scope of information theory. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set.

scholarly journals Optimal design for population pk/pd models

2012 ◽  
Vol 51 (1) ◽  
pp. 115-130
Author(s):  
Sergei Leonov ◽  
Alexander Aliev
Keyword(s):  
Optimal Design ◽  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Optimal Sampling ◽  
Design Algorithm ◽  
Sampling Schemes ◽  
Population Pk ◽  
Different Types

ABSTRACT We provide some details of the implementation of optimal design algorithm in the PkStaMp library which is intended for constructing optimal sampling schemes for pharmacokinetic (PK) and pharmacodynamic (PD) studies. We discuss different types of approximation of individual Fisher information matrix and describe a user-defined option of the library.

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