Abstract
Estimation of aircraft weight in flight, which is a dominant parameter related to its flight performance, was studied. A typical approach for the estimation is to use a simple combination of initial weight on the ground and fuel consumption in air obtained with fuel tank gauge or by accumulating fuel flow. This approach is insufficient when the flight performance will be estimated as accurately as possible because some of the available measurements are not utilized. Therefore, the forward–backward smoother derived from Kalman filter was applied to the estimation with our revision of the smoother to fuse terminal weight on the ground additionally. According to numerical simulations, our method estimated the weight in smaller errors than not only the typical approach but also a fixed-interval smoother, which is used generally for an off-line estimation problem. Moreover, application to actual flight data showed that our method improved the estimated standard deviation by approximately three percent at maximum compared to the fixed-interval smoother.
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References
Raymer D (2012) Aircraft Design: A Conceptual Approach. American Institute of Aeronautics and Astronautics, Reston
U.S. Department of Transportation: Federal Aviation Administration. Advisory Circular 120-27, Aircraft Weight and Balance Control
Karasawa K, Kato K (1991) An experiment on the weight vs control relations of subsonic airplanes. J Jpn Soc Aeronaut Space Sci 39(455):651–661
Kimberlin RD (January 2003) Flight Testing of Fixed-Wing Aircraft. American Institute of Aeronautics and Astronautics, Reston
Naruoka M, Ninomiya T, Uchiyama T, Yamada K, Sawada H, Ueno M (2019) Research activity of weight estimation of aircraft in flight; a case of research aircraft Hisho. In: The Japan Society for Aeronautical and Space Sciences the 57th Hikouki Sinpoziumu, 3A11
Simon D (2006) Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. John Wiley & Sons, Hoboken
Chati YS, Balakrishnan H (2018) Modeling of aircraft takeoff weight using Gaussian processes. J Air Transp 26(2):70–79
Alligier R, Gianazza D, Durand N (2015) Machine learning and mass estimation methods for ground-based aircraft climb prediction. IEEE Trans Intell Transp Syst 16(6):3138–3149
Sun J, Ellerbroek J, Hoekstra JM (2018) Aircraft initial mass estimation using Bayesian inference method. Transp Res Part C: Emerg Technol 90:59–73
Chaves FAV, Silvestre MAR, Gamboa PV (2018) Preliminary development of an onboard weight and balance estimator for commercial aircraft. Aerosp Sci Technol 72:316–326
Katatama T (2000) Shinban Ouyou Kalman Filter, Asakurashoten
Tomita H, Naruoka M (2014) Jikkenyou Koukuki Hisho nitsuite. J Jpn Soc Aeronaut Space Sci 62(6):195–201
Welch G, Bishop G et al (1995) An introduction to the kalman filter
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Ishikawa, A., Naruoka, M., Ninomiya, T., Adachi, S. (2023). Weight Estimation of Aircraft in Flight by Sensor Fusion with Revised Forward–Backward Smoother. In: Lee, S., Han, C., Choi, JY., Kim, S., Kim, J.H. (eds) The Proceedings of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), Volume 2. APISAT 2021. Lecture Notes in Electrical Engineering, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-19-2635-8_59
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DOI: https://doi.org/10.1007/978-981-19-2635-8_59
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