UAV Height Measurement Algorithm Based on Covariance Intersection and Three-Step Delay Time Fusion

With the rapid expansion of uncrewed aerial vehicles (UAVs) technology, these UAVs are now extensively utilized in both military and civilian sectors. UAV terrain following, a subset of flight control technology, has found extensive use in several fields, including topographical mapping, agricultural planting, and demining surveys. A good height-measuring technique is vital in complicated application environments with high requirements for precision in height measurement. This work provides a data fusion algorithm that combines many sensors at different heights to address the limitations of using a single height sensor in various application scenarios. First, the data collected from the sensors are initially processed using a three-step delayed time fusion algorithm, which considers a larger amount of historical data. Then, a covariance intersection fusion algorithm is used to fuse data from different sources, and the data are further complementarily fused by using an accelerometer, improving the height measurement accuracy. Finally, a Savitzky–Golay harmonic analysis and time-series filtering technique is utilized to efficiently handle data that contain both noise and missing values. The results indicate that the technique presented in this research, when compared with a similar approach, decreases the root-mean-square error by 15.21% and reduces the maximum error by 0.0336 m. The UAV height measurement algorithm is cost-effective, highly accurate, and reliable.