Estimation of motion-induced modulation transfer functions for airborne sensors

For field-deployed sensors, it is important to measure the modulation transfer function (MTF) under actual operational conditions, especially when the sensor is mounted on a moving platform such as an unmanned aerial vehicle (UAV). Mechanical vibrations, flight dynamics, and linear motion along the flight path can degrade system performance and cause image blur. Different UAV types-such as multirotor, vertical takeoff and landing (VTOL), and fixed-wing platforms-exhibit varying levels of motion blur due to their operating characteristics. These platforms typically operate at high speeds, making linear motion blur a primary limiting factor in performance. Direct measurement of the component MTFs corresponding to the three main sources of motion blur is not feasible for these platforms. Therefore, understanding the impact of platform-induced degradation on resolution performance is crucial when designing sensor systems. Predicting the MTF from platform-based measurements, such as those from an inertial navigation system (INS), has been a long-standing challenge. Previous efforts to estimate the component MTFs for each type of motion have shown significant errors compared to measured results. In this study, low-frequency motion is incorporated into the linear motion component MTF rather than treated as a separate component. This approach yields a close match between estimated and measured system MTFs, significantly reducing prediction error.