Aircraft initial mass estimation using Bayesian inference method |
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| Institution: | 1. Department of Civil and Environmental Engineering, Institute of Transportation Studies, 107 McLaughlin Hall, University of California, Berkeley, Berkeley, CA 94720, USA;2. Department of Civil and Environmental Engineering, Institute of Transportation Studies, 114 McLaughlin Hall, University of California, Berkeley, Berkeley, CA 94720, USA;1. Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China;2. State Environmental Protection Key Laboratory of Cause and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China;3. Chinese Research Academy of Environmental Sciences, Beijing 100012, China;1. State Institute of Aviation Systems, 7 Viktorenko str., Moscow 125319, Russia;2. Gromov Flight Research Institute, 2A Garnaeva str., Zhukovsky 140180, Russia;3. Moscow Aviation Institute (National Research University), 4 Volokolamskoe shosse, Moscow 125310, Russia |
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| Abstract: | Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass. |
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| Keywords: | Aircraft mass Weight estimation Bayesian inference |
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