TABLE OF CONTENTS The preceding chapter examined in considerable detail how to properly design and conduct a flight test to generate a high-quality database for use in system identification and model validation. Regardless of how much care is taken in instrumentation and flight testing, there will often exist kinematic inconsistencies between independent measurements of dynamically related variables. This chapter covers typical sources of data inconsistencies and presents both sophisticated and simple methods for isolating and correcting for such errors.
Inconsistencies arising from systematic or deterministic sources can range in complexity from simple differences in sign convention or measurement system calibration errors to a difficult combination of unknown biases and drifts in attitude and rate gyro measurements. Random or nondeterministic sources of data inconsistency include data dropouts, electrical noise, and air data disturbances, just to name a few of the practical glitches to be expected in the flight database. Data consistency and reconstruction techniques evaluate the flight-test data set as a whole to detect, model, and resolve these potential problems prior to formal system identification (Fig. 2.1). If not resolved at this point, countless hours can be wasted in an attempt to identify a consistent mathematical model from a kine-matically inconsistent data set.
Pioneering work in the field of kinematic consistency analysis and flight data reconstruction was done by Gerlach,95 Wingrove,96 Klein and Schiess,97 and Breeman and Simons.98 Methods of instrumentation error modeling, data consistency, and data reconstruction with excellent flight-test examples are well covered in the AGARD Report on Rotorcraft...
