You validated your model on simulated and drone-measured data. How much does the recognition accuracy drop when the drones flight altitude goes beyond the 4 to 15 meter range used in your experiments?
Thank you for your insightful question. In our current study, the drone-mounted experiments were conducted within a hover altitude range of 4 to 15 meters, which was selected to reflect typical operating conditions for low-altitude terrain sensing. While we have not yet systematically evaluated the models performance outside this range, we can infer potential behavior based on the design of our approach. The GAN-based model is trained on power spectral features extracted from echo signals, which are influenced by altitude primarily through changes in signal attenuation and multipath effects. Within the tested range, the model demonstrated stable and high classification accuracy (over 98%), suggesting robustness to altitude variations within this interval. If the flight altitude exceeds 15 meters, we anticipate a gradual decline in recognition accuracy, mainly due to the reduced signal-to-noise ratio (SNR) and increased beam footprint, which may smooth out fine-grained terrain features. Conversely, at altitudes below 4 meters, the signal may become overly sensitive to small-scale surface irregularities, potentially introducing noise and reducing generalization. That said, thanks to the domain adaptation capability of GANs and the use of simulated data covering a wider range of altitudes (4–15 m in 1 m increments) during training, the model may still generalize reasonably beyond the tested range. Nevertheless, further experiments at extended altitudes would be valuable to quantify the exact performance drop and to refine the model for broader operational envelopes. We are currently planning such experiments as part of future work to validate the models altitude generalization limits.
