Table 1.
Characteristics of different filtering approaches in UAV state estimation. Methods based on [25,12,8], and [24].
Fig 1.
UAV tracking control schematic.
System architecture showing sensor placement, coordinate systems, and interference sources affecting range-bearing measurements in planar tracking scenarios.
Fig 2.
Comprehensive system architecture.
Processing pipeline from trajectory generation through Monte Carlo evaluation, highlighting modular design for systematic filter comparison under controlled interference scenarios.
Fig 3.
Comprehensive performance analysis of different filtering approaches.
Multi-panel comparison: (a) Mean RMSE with error bars, (b) Box plots showing distributions, (c) APF stability analysis, (d) Interference sensitivity trends.
Table 2.
Comprehensive performance metrics across interference conditions.
Fig 4.
UAV trajectory comparison showing actual tracking performance.
Ground truth path (dashed) versus filter estimates during circular maneuver with magnified inset highlighting critical tracking differences and spatial reference markers.
Fig 5.
Performance degradation patterns under varying interference conditions.
Heat maps showing RMSE sensitivity to interference probability and magnitude parameters, with individual color scales emphasizing relative performance differences.
Fig 6.
Statistical analysis of APF performance improvements.
Paired t-test results and effect sizes demonstrating statistically significant advantages over conventional filtering approaches across all evaluation metrics.
Table 3.
Computational performance comparison of different filtering methods.
Table 4.
APF performance under different interference conditions. The results demonstrate the filter’s remarkable stability across varying interference levels, with only minimal degradation (<1 m RMSE increase) even under high interference conditions. This stability stands in marked contrast to the exponential degradation observed in traditional approaches.