#### Abstract

This article discusses the issue of choosing meta parameters when solving an ill-conditioned identification problem. The influence of the length of the sliding interval and the choice of the target functional is studied. The problem is studied using the example of the problem of separate identification of thrust and drag force coefficient. The results of applying the described approaches to analyze data obtained on aircraft flight simulation facilities are presented. This article examines the issue of choosing meta parameters when solving an ill-conditioned identification problem, the main feature of which is that small errors in the input data greatly affect the final result. There are many heuristic approaches to solving this type of problem. This work examines the influence of the length of the sliding interval and the choice of the target functional. The problem is considered using the example of separate identification of thrust force and drag force coefficients, which is an imminent part of aircraft flight tests, since the value of thrust characterizes their operational capabilities in an essential way. Therefore, estimation of the thrust value is a mandatory stage of flight testing. To regularize the problem, a special test flight maneuver is proposed, based on the following considerations. Let us assume that the thrust force remains constant at a constant engine operating mode, a constant altitude and a small change in flight speed. Then, to ensure the identifiability of the system, it is necessary to carry out speed changes, the amplitude of which is small compared to the steady-state value, so that the thrust could be considered constant during the processing interval. In particular, changing the speed at a constant engine operating mode may be achieved by performing a series of dives and pitches with a small trajectory inclination. Using the data obtained at the simulation bench, it was demonstrated that with noises close to those observed during flight experiments, it is possible to obtain estimates with sufficiently high accuracy.

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