Geometry-Based Drift Compensation for Distributed Channel Sounding Measurements in Dynamic Drone Scenarios

Abstract

Measured impulse responses obtained from a dynamic unmanned aerial vehicle (UAV) channel sounding system exhibit effects attributable to time-varying carrier frequency offset (CFO) and sampling frequency offset (SFO). To correct the recorded data in post-processing, we extend existing geometry-based drift compensation algorithms by an explicit line-of-sight (LoS) determination, combining a symbol-wise high-resolution parameter estimation (HRPE) in delay with a Kalman filter. This proposed extension facilitates the removal of rapidly varying synchronization mismatches from channel sounding measurements in rich multipath propagation scenarios. Furthermore, we propose using the relative residual power after subtraction of estimated multipath components as a metric for ground-truth-independent comparison of post-processing synchronization methods for recorded channel sounding data. The application of the proposed procedure shows that our approach outperforms existing post-processing compensation algorithms, reducing the relative residual power by more than 5 dB and the delay-Doppler estimate root mean square errors (RMSEs) of a passive UAV target by approximately 60 %.