Aircraft Takeoff and Landing Weight Estimation from Surveillance Data

Abstract

Aircraft weight estimation is a common problem facing researchers working with aircraft surveillance data. Although knowledge of an aircraft’s weight and thrust is required for many types of analyses, such as those evaluating aircraft acoustic noise, fuel burn, and emissions, these parameters are typically not available from surveillance sources. Instead, researchers generally only have access to basic aircraft states: lateral position, groundspeed, and altitude. Therefore, methods for estimating the weight of aircraft from these basic states become necessary in cases where aircraft performance is a key component of the analysis. This paper introduces two weight estimation models: one for the estimation of aircraft takeoff weight from departure data, and another for the estimation of aircraft landing weight from arrival data. The models are mathematically simple but grounded in knowledge of aircraft certification, airline operations, and aircraft flight management system logic. The landing weight estimation model proposed is shown to have a mean absolute error equivalent to 2.66% of maximum takeoff weight and a standard deviation of 3.35% of maximum takeoff weight when validated using onboard data recordings from 240 Airbus A320 flights. Similarly, the proposed takeoff weight estimation model is shown to have a mean absolute error of 2.83% of the maximum takeoff weight and a standard deviation of 3.55% of the maximum takeoff weight when applied to the same validation dataset.