Parameter Estimation of a Tactical Missile Using Linear Regression

Name: Parameter Estimation of a Tactical Missile Using Linear Regression
Brand: Biblioscholar
SKU: 9781249600275
Price: 87.8 RON
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Autor: Kelly S Powers

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Editura: Biblioscholar

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The purpose of this research was to present the method of Linear Regression as a parameter identific...

Informații despre carte

Autor

Kelly S Powers

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2012

Pagini

102

EAN

9781249600275

ISBN

9781249600275

Enbook ID

08142805

Editura

Biblioscholar

Greutate

195

Dimensiuni

189 x 246 x 5

Descriere completă

The purpose of this research was to present the method of Linear Regression as a parameter identification method to determine the longitudinal dimensional stability derivatives of a tactical missile. Missile flight histories are characterized by rapid accelerations, rapidly changing mass property characteristics with often short flight times. These characteristics make accurate parameter estimation of the missile aerodynamics more challenging than for aircraft. The simulation used for this research was created in MATLAB/SIMULINK based on the missile trajectory program, TRAP. The aerodynamic data for the 6-DoF missile model was based on a supersonic, tail controlled missile similar to an AIM-9X missile. Two command input types were investigated to determine if either could induce an excitation of the system modes of the plant being measured to lead to good estimates of the model parameters. These two input types were a high-frequency pitch doublet and band-limited white noise. The research indicated the conclusion: While the method is not as complex as other parameter estimation methods, this research shows that linear regression can be used successfully in determining longitudinal dimensional stability derivatives of a tactical missile in flight when using a control input form with higher frequency modulations, such as band-limited or filtered white noise.

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Parameter Estimation of a Tactical Missile Using Linear Regression

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