Sampling-Based Adaptive Techniques for Reducing Non-Gaussian Position Errors in GNSS/INS Systems

In this paper, we propose a novel method to reduce non-Gaussian errors in measurements using sampling-based distribution estimation. Although non-Gaussian errors are often treated as statistical deviations, they can frequently arise in practical unmanned aerial systems that depend on global navigation satellite systems (GNSS), where position measurements are degraded by multipath effects. However, nonlinear or robust filters have shown limited effectiveness in correcting such errors, particularly when they appear as persistent biases in measurements over time. In such cases, adaptive techniques have often demonstrated greater effectiveness. The proposed method estimates the distribution of observed measurements using a sampling-based approach and derives a reformed measurement from this distribution. By incorporating this reformed measurement into the filter update, the proposed approach achieves lower error levels than traditional adaptive filters. To validate the effectiveness of the method, Kalman filter simulations are conducted for drone GNSS/INS navigation. The results show that the proposed method outperforms conventional non-Gaussian filters in handling measurement bias caused by non-Gaussian errors. Furthermore, it achieves nearly twice the estimation accuracy compared to adaptive approaches. These findings confirm the robustness of the proposed technique in scenarios where measurement accuracy temporarily deteriorates before recovering.