Assessment of drag prediction techniques based on radar data for supersonic veh

Document Type : Original Article


Aerospace Engineering Department, Military Technical College, Cairo, Egypt.



Flight testing is probably the most accurate approach for defining the aerodynamic characteristics of a flying vehicle. This is justified by the fact that compared to other approaches, either engineering or computational ones, flight testing is indeed the one that perfectly resembles the real flight environment. Flight data are either obtained by measuring the vehicle's kinematics via onboard sensors or by tracking the vehicle's flight via (commonly) radars. The advantages of the latter approach are evident in cases where modifying the vehicle design is not possible or in cases where rival/enemy vehicles are examined. The key issue for this approach is the quality and demands of the technique by which vehicle aerodynamic characteristics are reduced from flight-tracked data. In the open literature, different techniques are used to analyze radar data of vehicle position and utilize them to predict vehicle drag coefficient. Each technique has its strengths and pitfalls. In this paper, the three well-used techniques of drag estimation from radar data namely, Least Square (LS), Maximum Likelihood Estimation (MLE), and Stepwise regression (SR), are considered. The underlying principle, the output, and the range of validity for each technique are addressed with emphasis on what differentiates each of them. The viability and validity of the three techniques are addressed based on the own flight testing of a free-flight supersonic vehicle and using the point-mass flight model. Meteorological data are also recorded and flight conditions are used to enhance the resulting calculations. Based on experimental data available from the literature, a comparison is conducted for the techniques examined. Considering the lack of flight data utilized, and in conjunction with data from the literature, it has been concluded that the SR technique outperforms for a higher sample rate, however, the MLE is more feasible when there is a lack of data.

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