Meteoroid mass estimation based on single‐frequency radar cross section measurements

Meteoroid mass estimation based on single-frequency radar cross section measurements

10.1002/essoar.10505271.1 ◽

2020 ◽

Author(s):

Liane Kathryn Tarnecki ◽

Robert Marshall

Keyword(s):

Cross Section ◽

Radar Cross Section ◽

Single Frequency ◽

Mass Estimation

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Meteoroid mass estimation based on single-frequency radar cross section measurements

10.1002/essoar.10506963.1 ◽

2021 ◽

Author(s):

Liane Kathryn Tarnecki ◽

Robert Andrew Marshall ◽

Gunter Stober ◽

Johan Kero

Keyword(s):

Cross Section ◽

Radar Cross Section ◽

Single Frequency ◽

Mass Estimation

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Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

Remote Sensing ◽

10.3390/rs12111903 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1903

Author(s):

Cheng Hu ◽

Shaoyang Kong ◽

Rui Wang ◽

Fan Zhang ◽

Lianjun Wang

Keyword(s):

Support Vector Regression ◽

Cross Section ◽

Radar Cross Section ◽

Estimation Methods ◽

Estimation Accuracy ◽

Support Vector ◽

Feature Subset ◽

Mass Estimation ◽

Optimal Feature Subset ◽

Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

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Assessment and Validation of Collision “Consequence” Method of Assessing Orbital Regime Risk Posed by Potential Satellite Conjunctions

2019 15th Hypervelocity Impact Symposium ◽

10.1115/hvis2019-061 ◽

2019 ◽

Author(s):

Travis F. Lechtenberg ◽

Matthew D. Hejduk

Keyword(s):

Risk Assessment ◽

Risk Management ◽

Cross Section ◽

Radar Cross Section ◽

Management Theory ◽

Space Craft ◽

Collision Risk ◽

Mass Estimation ◽

Estimation Process

Abstract Collision risk management theory requires a thorough assessment of both the likelihood and consequence of potential collision events. Satellite conjunction risk assessment has produced a highly-developed theory for assessing the likelihood of collision but typically neglects to account for the consequences of a given collision. While any collision may compromise the operational survival of a space-craft, the amount of debris produced by the potential collision, and therefore the degree to which the orbital corridor may be compromised, can vary greatly among satellite conjunctions. Previous studies leveraged work on satellite collision modeling to develop a method to estimate whether a particular collision is likely to produce a relatively large or relatively small amount of resultant debris. The approximation of the number of debris pieces is dependent on a mass estimation process for the secondary objects utilizing the radar cross section of said object. This study examines the validity of the mass estimation process and establishes uncertainty bounds on the secondary object mass which will be used to best approximate the possible consequences of a prospective collision.

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